BM25 — The Ranking Algorithm Behind Modern Search

Introduction: Why BM25 Matters

Every search begins with a question: how do we find the most relevant information from thousands—or even millions—of documents? Whether you’re searching for a product online, looking up an answer in a knowledge base, or retrieving documents for a large language model (LLM), the challenge is the same: identifying which results best match your query.

This is where BM25 (Best Matching 25) comes in. BM25 is a widely used ranking algorithm in information retrieval that scores documents based on how well their content matches a search query. It considers factors such as how frequently query terms appear in a document, how informative those terms are across the document collection, and how the document’s length compares with the average.

Despite the growing popularity of AI-powered semantic search, BM25 remains a fundamental technique in modern search systems. Its effectiveness, efficiency, and interpretability make it a strong baseline for retrieving relevant documents. It is also widely used in systems such as Elasticsearch and OpenSearch, and it plays an important role in retrieval-augmented generation (RAG), where relevant documents are retrieved before an LLM generates a response.

In this article, we’ll explore how BM25 works, break down its scoring formula, compare it with TF-IDF, and implement a simple BM25 search system in Python. By the end, you’ll understand why this algorithm remains relevant and when to use it to build better search experiences.

What Is BM25?

BM25 (Best Matching 25) is a ranking algorithm used in information retrieval to determine how relevant a document is to a given search query. It assigns a numerical score to each document based on the query terms it contains, helping search systems rank the most relevant results higher.

BM25 is based on the probabilistic information retrieval framework and builds on earlier approaches to document ranking, including TF-IDF. While TF-IDF measures the importance of terms using their frequency in a document and across a collection, BM25 introduces additional refinements that make its scoring more effective in many practical search scenarios.

At its core, BM25 evaluates relevance using three main factors:

• Term frequency (TF): How often a query term appears in a document. Documents containing relevant query terms tend to receive higher scores, but the benefit of repeating a term decreases as its frequency increases.
• Inverse document frequency (IDF): How informative a query term is across the entire document collection. Rare terms generally contribute more to a document’s score than common terms because they help distinguish relevant documents from others.
• Document length normalization: How long a document is compared with the average document length. This helps prevent longer documents from receiving an unfair advantage simply because they have more opportunities to contain query terms.

Consider a search query such as “machine learning applications.” A document dedicated to machine learning applications is likely to rank higher than a lengthy article about computer science that mentions the topic only briefly. BM25 helps make this distinction by combining term frequency, term rarity, and document length into a single relevance score.

One of BM25’s key strengths is its simplicity. It does not require training a neural network or generating vector embeddings. Instead, it works directly with indexed text, making it computationally efficient and relatively straightforward to implement.

Today, BM25 is commonly used in search platforms such as Elasticsearch and OpenSearch, enterprise search systems, and retrieval pipelines for retrieval-augmented generation (RAG). Although it does not inherently understand synonyms or semantic meaning, it provides a strong foundation for keyword-based search and often complements modern semantic retrieval techniques.

How Does BM25 Work?

BM25 works by calculating a relevance score for each document based on how closely its content matches a search query. Rather than simply counting how many times a query term appears, it considers the frequency of each term, how rare the term is across the document collection, and the length of the document.

The goal is to reward documents that contain meaningful matches without giving an excessive advantage to long documents or those that repeat the same keywords many times.

Let’s break down the three main components of BM25 and see how they work together.

Term Frequency: More Occurrences, More Relevance

Term frequency measures how often a query term appears in a document. Intuitively, if a document mentions a search term several times, it may be more relevant to that query than a document that mentions it only once.

However, BM25 uses term frequency saturation, meaning that each additional occurrence contributes less to the score than the previous one.

For example, consider a search query for “machine learning” and two documents:

• Document A: Mentions “machine learning” twice.
• Document B: Mentions “machine learning” fifteen times.

Document B may be more relevant, but it should not automatically receive a score 7.5 times higher simply because it repeats the term more frequently. Repetition does not necessarily mean greater relevance.

BM25 addresses this by using a saturation function that increases the score as term frequency rises, but gradually approaches a limit. This reduces the impact of keyword stuffing and prevents repeated terms from dominating the ranking.

The parameter k_1 controls how quickly term frequency saturates. Higher values generally allow term frequency to influence the score over a wider range.

Inverse Document Frequency: Rewarding Distinctive Terms

Not all search terms are equally informative. A term that appears in nearly every document is generally less useful for distinguishing relevant results from irrelevant ones than a rare, specific term.

This is the purpose of inverse document frequency (IDF).

BM25 assigns greater weight to terms that occur in fewer documents across the collection. Common terms contribute less, while distinctive terms can have a greater influence on the final relevance score.

For example, imagine a collection of technical articles and a search query containing the terms “software” and “vector database.”

• The word “software” might appear in thousands of documents, making it less useful for distinguishing one result from another.
• The phrase “vector database” may appear in far fewer documents, making it more informative for identifying relevant content.

A commonly used BM25 IDF formula is:

Where:

• N is the total number of documents in the collection.
• n(q_i) is the number of documents containing the query term q_i.
• 0.5 is a smoothing constant that helps stabilize the calculation.

This formula gives more weight to terms that appear in fewer documents. The precise IDF formulation can vary across BM25 implementations.

Document Length Normalization

Long documents naturally have more opportunities to contain query terms. Without an adjustment for document length, they could receive higher scores simply because they contain more text.

BM25 addresses this problem through document length normalization.

It compares a document’s length with the average length of documents in the collection. Longer-than-average documents are generally penalized, while shorter documents may receive a relative advantage, depending on their term frequencies and the normalization settings.

The parameter b controls the strength of this adjustment:

• b=0: No document length normalization.
• b=1: Full length normalization according to the BM25 formula.
• 0<b<1: Partial length normalization.

For example, suppose two documents discuss the same topic. One is a concise, 500-word explanation, while the other is a 5,000-word article that mentions the topic only briefly. Length normalization helps prevent the longer article from receiving an automatic advantage merely because it contains more words.

In practice, document length normalization is especially useful when searching collections containing documents of very different sizes, such as articles, reports, product descriptions, and technical documentation.

The BM25 Formula

BM25 combines term frequency, inverse document frequency, and document length normalization into a single scoring function.

A commonly used version of the formula is:

Let’s examine what each component means.

Symbol	Meaning
D	The document being scored
Q	The search query
q_i	A query term
f(q_i,D)	Frequency of the query term in the document
IDF(q_i)	Inverse document frequency of the query term
k_1	Controls term frequency saturation
b	Controls document length normalization
|D|	Length of the document
avgdl	Average document length in the collection

The summation means that BM25 calculates a contribution for each query term and adds those contributions together to produce the document’s final score.

The formula combines two important behaviors:

1. Term frequency saturation: Repeated occurrences increase relevance, but with diminishing returns.
2. Length normalization: The contribution of a term is adjusted based on the document’s length relative to the collection average.

The IDF component then determines how much each query term matters. A distinctive term can contribute more to the final score than a common one.

The parameters k_1 and b are often set to values around 1.2 and 0.75, respectively, although appropriate values depend on the dataset, implementation, and retrieval task.

Putting It All Together

Imagine a user searches for “machine learning applications.” A BM25-based search system evaluates each document against the query.

• A document containing the query terms multiple times may receive a higher score because of its term frequency.
• A document containing distinctive query terms may benefit from their IDF weights.
• A document’s length affects how much its term frequencies contribute to the final score.

BM25 combines these factors to rank documents by their estimated relevance to the query. The document with the highest score is ranked first, followed by documents with lower scores.

Importantly, BM25 does not understand a document’s meaning in the same way a language model does. It primarily evaluates lexical matches and their statistical importance. This makes it efficient and effective for many keyword-based retrieval tasks, but it can struggle when a relevant document uses synonyms or different wording from the query.

Understanding these three components provides the foundation for seeing why BM25 often outperforms simpler term-matching approaches. In the next section, we’ll walk through a practical example to see how BM25 scores and ranks documents in a real search scenario.

A Practical BM25 Example

Now that we understand how BM25 works, let’s look at a practical example to see how it ranks documents based on a search query. By comparing a few documents, we can see how term frequency, inverse document frequency, and document length normalization influence search results.

Defining the Search Query

Imagine we’re building a search engine for a collection of technical articles. A user enters the following query:

Search query: “machine learning applications”

Our search engine needs to identify which documents are most relevant to this query and rank them accordingly.

Suppose our document collection contains the following three documents:

Document	Content	Length
Document A	A focused guide explaining machine learning applications in healthcare and finance.	Short
Document B	A comprehensive overview of computer science, covering programming, databases, networking, and other topics, with a brief section on machine learning applications.	Long
Document C	An introduction to web design, covering HTML, CSS, responsive layouts, and user experience.	Medium

Based on their content, we would expect Document A to rank first, Document B to rank second, and Document C to rank last.

But how does BM25 arrive at this ranking?

Step 1: Evaluate Term Frequency

First, BM25 examines how frequently the query terms appear in each document.

Document A focuses on machine learning applications, so the query terms are likely to appear prominently in its content. Document B mentions the topic only briefly, while Document C contains none of the query terms in this simplified example.

Documents with more occurrences of matching terms can receive higher scores. However, BM25 uses term frequency saturation, so repeating a word many times does not increase the score indefinitely.

This means Document A can receive a strong relevance score without needing to repeat the query terms excessively.

Step 2: Consider Inverse Document Frequency

Next, BM25 evaluates how informative each query term is across the entire document collection.

Suppose the collection contains many documents about general computer science but relatively few that discuss machine learning applications specifically. The more distinctive query terms can contribute more to the relevance score because they help distinguish relevant documents from unrelated ones.

The IDF value depends on the entire indexed collection, not just the three documents shown in this example.

This distinction matters because a term that is rare in one collection may be common in another. As a result, the same query can produce different scores when used against different document collections.

Step 3: Apply Document Length Normalization

Document length also influences the final score.

Document B is much longer than Document A. Because it covers many different topics, it has more opportunities to contain the query terms. BM25 accounts for this by adjusting term contributions according to document length relative to the collection’s average.

This adjustment helps prevent long documents from automatically outperforming shorter, more focused documents.

Document A may therefore score higher because it matches the query well without requiring a large amount of unrelated content.

Step 4: Calculate Scores and Rank the Documents

BM25 combines term frequency, IDF, and document length normalization to calculate a score for each document.

The following table illustrates the expected ranking for our example.

Rank	Document	Expected BM25 score behavior	Reason
1	Document A	Higher	Focused content with strong query-term matches
2	Document B	Lower than A	Contains the query terms but covers many unrelated topics
3	Document C	Zero in this simplified example	Contains none of the query terms

These are qualitative expectations, not calculated BM25 scores. Actual numerical scores depend on the complete document collection, tokenization, term frequencies, document lengths, and the chosen BM25 implementation.

In particular, Document C receives a zero score under the standard lexical BM25 formulation described here because none of its terms match the query. BM25 does not inherently recognize that an article about web design might be indirectly related to machine learning.

What Does This Example Teach Us?

This example highlights three important characteristics of BM25:

• Relevance matters more than repetition: Additional term occurrences help, but their contribution diminishes.
• Distinctive terms carry more weight: Rare query terms can help identify particularly relevant documents.
• Document length matters: Length normalization reduces the advantage that longer documents might otherwise receive.

Together, these mechanisms make BM25 an effective baseline for keyword-based search. However, its reliance on lexical matching also creates limitations: a document can be highly relevant in meaning yet receive a low score if it uses different words from the query.

For example, a document about “AI-driven medical diagnosis” might be relevant to a search for “machine learning applications in healthcare” even if the exact query terms appear infrequently or not at all.

This is one reason modern search systems often combine BM25 with semantic retrieval, which can identify conceptually related content even when the wording differs.

BM25 vs. TF-IDF: What’s the Difference?

TF-IDF (Term Frequency–Inverse Document Frequency) and BM25 are two popular approaches to ranking documents in information retrieval. Both estimate how relevant a document is to a search query by considering the terms it contains and how informative those terms are across a document collection.

However, BM25 builds on the ideas behind TF-IDF by introducing term frequency saturation and a more explicit approach to document length normalization. These refinements often make BM25 a stronger baseline for practical search applications.

To understand why, let’s examine how the two approaches work and where each one is useful.

What Is TF-IDF?

TF-IDF is a statistical weighting technique that measures how important a term is to a document relative to a collection of documents.

It combines two components:

• Term frequency (TF): Measures how frequently a term appears in a document.
• Inverse document frequency (IDF): Measures how informative a term is based on how many documents contain it.

The basic idea is straightforward: a term is important when it appears frequently in a particular document but is relatively uncommon across the overall collection.

A simplified TF-IDF formula is:

Where t represents a term and D represents a document.

For example, if the word “database” appears frequently in a technical article but infrequently across the collection, it may receive a relatively high TF-IDF weight.

TF-IDF is simple, computationally efficient, and useful for tasks such as document similarity, keyword extraction, and basic search ranking. However, its behavior depends on the particular term-frequency definition, IDF formula, and document normalization method being used.

How Does BM25 Improve on TF-IDF?

BM25 uses similar statistical principles but introduces refinements designed to improve document ranking.

1. Term frequency saturation

In basic TF-IDF implementations that use raw term frequency, every additional occurrence contributes the same amount to the term-frequency component. Other TF-IDF variants use logarithmic or normalized frequency, so this behavior is not universal.

BM25 explicitly models diminishing returns through a saturation function. As a term appears more frequently, its additional contribution gradually decreases.

This helps prevent documents from dominating search results simply because they repeat a query term many times.

2. Document length normalization

TF-IDF does not prescribe one universal document length normalization method. Some implementations normalize document vectors, while others use alternative approaches.

BM25 includes document length normalization directly in its standard scoring formula. It adjusts term contributions based on the document’s length relative to the average length of the collection.

This can make BM25 particularly useful when ranking documents of varying lengths, such as articles, reports, and technical documentation.

3. Tunable ranking behavior

BM25 includes two important parameters:

• k_1 controls how quickly term frequency saturates.
• b controls the strength of document length normalization.

These parameters allow practitioners to adjust ranking behavior to suit a particular retrieval task. The best settings depend on the dataset and should ideally be selected through evaluation.

BM25 vs. TF-IDF: A Side-by-Side Comparison

Feature	TF-IDF	BM25
Main purpose	Weight terms by their importance	Rank documents by query relevance
Term frequency	Depends on the chosen TF variant	Explicit saturation
IDF weighting	Yes	Yes, with a BM25-specific formulation
Document length normalization	Depends on implementation	Built into the standard formula
Tunable parameters	Depends on the implementation	k_1 and b
Computational cost	Generally low	Generally low
Typical applications	Keyword weighting, similarity, basic retrieval	Search engines, document retrieval, lexical search
Semantic understanding	No	No

An important distinction is that TF-IDF is primarily a term-weighting scheme, whereas BM25 is a ranking function designed for information retrieval. Both can be used to rank documents, but their scoring mechanisms differ.

A Practical Example

Imagine a user searches for:

Query: “machine learning applications”

Consider two documents:

• Document A: A focused 500-word article about machine learning applications in healthcare.
• Document B: A 5,000-word overview of computer science that mentions machine learning applications several times.

A basic TF-IDF approach using raw term frequencies may give Document B an advantage if the query terms appear more frequently in its text. However, the outcome depends on the TF variant, vector normalization, and document representation.

BM25 handles this situation differently. It limits the contribution of repeated query terms and adjusts their contributions according to document length. If Document A contains sufficiently strong and informative matches, it may rank higher despite being shorter.

Neither algorithm guarantees that Document A will win. The ranking depends on the actual term frequencies, IDF values, document lengths, and scoring configuration.

The key takeaway is that BM25 is designed to balance term matching with diminishing returns and document length normalization, making it a robust choice for many lexical retrieval systems.

When Should You Use BM25 vs. TF-IDF?

The right choice depends on what you’re building.

Choose TF-IDF when:

• You need a simple, interpretable term-weighting baseline.
• You’re measuring document similarity using vector representations.
• You’re performing keyword extraction or exploratory text analysis.
• You want a straightforward implementation for a small-scale experiment.

Choose BM25 when:

• You’re building a search engine that ranks documents for user queries.
• Your collection contains documents with substantially different lengths.
• You want explicit control over term frequency saturation and length normalization.
• You’re building a lexical retrieval stage for an enterprise search or RAG system.

For many practical search applications, BM25 is a strong starting point. However, TF-IDF remains useful, and neither approach is guaranteed to outperform the other on every dataset. The best choice should be determined by evaluating ranking quality on representative queries and relevance judgments.

Can BM25 and Semantic Search Work Together?

Yes. BM25 and semantic search solve related but different problems.

BM25 excels at matching specific terms, including product names, technical terminology, identifiers, and exact keywords. Semantic search uses representations such as embeddings to retrieve content based on conceptual similarity, even when the query and document use different wording.

For example, a BM25 search for “car repair” may favor documents containing those exact words. A semantic retrieval system may also find a relevant document about “automotive maintenance.”

Many modern retrieval systems combine both approaches through hybrid search, then optionally use a reranker to improve the ordering of the retrieved results.

This is particularly useful in retrieval-augmented generation (RAG), where finding relevant context can directly affect the quality of an LLM’s response.

How To Implement BM25 in Python

Understanding the theory behind BM25 is useful, but the best way to see how it works is to implement it in a real search example. Fortunately, Python provides libraries that make it straightforward to build a BM25-based retrieval system without implementing the ranking formula from scratch.

In this section, we’ll use the rank-bm25 library to index a small collection of documents, process a search query, calculate relevance scores, and return documents in ranked order.

Install the Required Library

We’ll use rank-bm25 , a lightweight Python library that implements BM25 ranking.

Install it using pip:

pip install rank-bm25

The library provides the BM25Okapi class, which implements a popular variant of BM25. It is suitable for learning the algorithm and building small retrieval prototypes.

Prepare a Collection of Documents

First, let’s create a small collection of documents that our search engine will retrieve from.

documents = [    
"BM25 is a ranking algorithm used in information retrieval.",    
"Machine learning improves search relevance and ranking.",    
"BM25 scores documents based on query terms and document length.",    
"Deep learning models can understand semantic relationships.",    
"Search engines use ranking algorithms to find relevant documents."
]

Each string represents a document in our collection. In a real application, these documents could come from blog posts, product descriptions, research papers, or an internal knowledge base.

Before indexing, we need to tokenize the documents. Tokenization converts each document into a sequence of terms that BM25 can process.

tokenized_docs = [
doc.lower().split()
for doc in documents
]

This example converts all text to lowercase and splits it on whitespace. That is sufficient for a basic demonstration, but production systems may require more sophisticated tokenization, punctuation handling, language-specific processing, and other normalization steps.

Importantly, the query must be processed consistently with the documents.

Build the BM25 Index

Next, we’ll initialize the BM25 model using our tokenized documents.

from rank_bm25 import BM25O
kapibm25 = BM25Okapi(tokenized_docs)

When the BM25Okapi object is created, the library calculates the statistics needed for ranking, including document frequencies and document lengths. These statistics are used to score documents against future queries.

Once the index is initialized, we can reuse it for multiple searches, provided the underlying document collection remains unchanged.

Search the Documents

Let’s search for documents related to BM25 and search relevance.

query = "BM25 search relevance"
tokenized_query = query.lower().split()
scores = bm25.get_scores(tokenized_query)
for i, score in enumerate(scores):
print(f"Document {i + 1}: {score:.3f}")

The get_scores() method calculates a BM25 score for every document in the collection.

A higher score generally indicates a stronger lexical match for the query. Documents containing informative query terms can score well, while documents without matching terms typically receive lower scores.

BM25 scores are relative to the indexed collection and scoring configuration. They are not probabilities, and a score of 2 does not necessarily indicate twice the relevance of a score of 1.

Rank the Documents by Relevance

Calculating scores is only part of the search process. We also need to sort the documents so that the most relevant results appear first.

Here’s how to implement that:

ranked_results = sorted(
zip(documents, scores),
key=lambda item: item[1],
reverse=True
)
for rank, (document, score) in enumerate(
ranked_results, start=1
):
print(f"{rank}. Score: {score:.3f}")
print(f"   {document}")

The zip() function pairs each document with its corresponding score. We then sort these pairs by score in descending order.

The resulting list contains the documents ranked from highest to lowest BM25 score.

For a real search application, you would typically return only the top \(k\) documents rather than the entire collection. For example, adding [:3] to the sorted results would return the top three matches.

Complete BM25 Implementation

For convenience, here’s the complete example in one place.

from rank_bm25 import BM25Okapi
# 1. Prepare documents
documents = [
"BM25 is a ranking algorithm used in information retrieval.",
"Machine learning improves search relevance and ranking.",
"BM25 scores documents based on query terms and document length.",
"Deep learning models can understand semantic relationships.",
"Search engines use ranking algorithms to find relevant documents."
]
# 2. Tokenize documents
tokenized_docs = [
doc.lower().split()
for doc in documents
]
# 3. Build the BM25 index
bm25 = BM25Okapi(tokenized_docs)
# 4. Process the search query
query = "BM25 search relevance"
tokenized_query = query.lower().split()
# 5. Calculate document scores
scores = bm25.get_scores(tokenized_query)
# 6. Rank documents
ranked_results = sorted(
zip(documents, scores),
key=lambda item: item[1],
reverse=True
)
# 7. Display the top 3 results
for rank, (document, score) in enumerate(
ranked_results[:3], start=1
):
print(f"{rank}. Score: {score:.3f}")
print(f"   {document}")

This implementation demonstrates the essential workflow of a BM25 search system: prepare documents, tokenize text, build an index, score documents against a query, and return ranked results.

Important Considerations for Production Systems

Although the example above is useful for learning, production search systems need additional considerations.

• Tokenization and normalization: Handle punctuation, language-specific rules, and other text-processing requirements consistently.
• Document updates: Rebuild or update the index when the collection changes, depending on the library or search engine.
• Parameter tuning: Evaluate different values of k_1 and b against representative search queries.
• Ranking evaluation: Use metrics such as nDCG, Mean Reciprocal Rank (MRR), and Recall@k to measure retrieval quality.
• Scalability: For large document collections, consider a dedicated search engine such as Elasticsearch or OpenSearch rather than loading all documents into a simple in-memory Python index.

It’s also worth noting that rank-bm25 is primarily a lightweight retrieval library, not a complete search platform. It does not provide the full indexing, persistence, distributed processing, and operational capabilities of a dedicated search engine.

Where Is BM25 Used?

BM25 is widely used in information retrieval systems to identify and rank documents that best match a user’s search query. Its efficiency, simplicity, and strong performance on keyword-based searches make it a popular choice across a range of applications, from traditional search engines to modern AI-powered retrieval systems.

Although semantic search and neural ranking models have become increasingly popular, BM25 remains an important component of many search architectures. Let’s explore some of its most common real-world applications.

Search Engines

One of the most common applications of BM25 is document ranking in search engines. When a user enters a query, the search system needs to retrieve relevant documents from an index and determine which results should appear first.

BM25 helps accomplish this by scoring documents based on query-term frequency, term rarity, and document length.

Search platforms such as Elasticsearch and OpenSearch use BM25 as a default similarity model for many standard full-text search configurations. Developers can use it to build search functionality for websites, applications, and large document collections.

For example, when a user searches for “best database indexing techniques,” BM25 can prioritize documents containing informative terms such as “database,” “indexing,” and “techniques,” while accounting for how frequently those terms appear and how long the documents are.

Its ability to provide fast, effective lexical ranking makes BM25 a strong baseline for search applications.

Enterprise Search and Knowledge Bases

Organizations often maintain large collections of internal documents, including technical documentation, support articles, policies, reports, and product manuals.

Finding relevant information across these collections can be challenging, particularly when users do not know exactly where a document is stored.

BM25 can power enterprise search systems by ranking documents according to their lexical relevance to a query.

For example, an employee searching for “database backup recovery procedure” might receive technical guides containing those terms near the top of the results.

BM25 is especially useful when queries contain specific terminology, product names, error messages, or identifiers that need to match the indexed text closely.

In these situations, exact lexical matching can be more reliable than semantic similarity alone.

E-Commerce and Product Search

Online retailers need to match customer queries with relevant products from potentially large catalogs. BM25 can help rank product listings by comparing search terms with product titles, descriptions, specifications, and other indexed fields.

For example, a customer searching for “wireless noise cancelling headphones” might receive products containing those terms in their titles or descriptions.

A product search system can also assign different weights to fields. For instance, a match in the product title may be more important than a match in a long description. This behaviour is typically implemented through the search engine’s field-boosting or multi-field scoring features rather than through BM25 alone.

BM25 is therefore useful for building keyword-based product discovery systems. However, product ranking often combines lexical relevance with other signals, such as availability, popularity, personalization, and business rules.

Retrieval-Augmented Generation (RAG)

One of the increasingly important applications of BM25 is retrieval-augmented generation (RAG).

RAG systems retrieve relevant information from an external knowledge source before passing that information to a large language model (LLM). This helps the model generate responses grounded in retrieved documents rather than relying exclusively on information learned during training.

BM25 can serve as the retrieval component in this pipeline.

Consider a user asking an internal AI assistant:

“How do I configure authentication for our API?”

The system searches its documentation collection using BM25, retrieves relevant passages, and provides them as context to the LLM. The model then uses that context to formulate an answer.

BM25 is particularly useful in RAG when users search for exact technical terms, configuration options, error codes, or product names.

However, it may miss relevant passages that use different wording from the query. For this reason, many RAG architectures combine BM25 with dense-vector retrieval in a hybrid search system.

This allows the system to benefit from both exact keyword matching and semantic similarity.

Digital Libraries and Academic Research

Academic search platforms and digital libraries contain large collections of research papers, abstracts, reports, and citations. Researchers often need to retrieve documents based on specific concepts, methods, authors’ terminology, or technical phrases.

BM25 can help rank research papers according to their textual relevance to a query.

For example, a researcher searching for “neural network optimization methods” may find papers containing these terms in their titles, abstracts, or full text.

Its effectiveness for keyword-based retrieval makes BM25 a useful component in scholarly search systems. However, academic retrieval may also require specialized ranking signals, including citation information, publication dates, metadata, and field-specific terminology.

Customer Support and Help Centers

Customer support platforms often contain thousands of troubleshooting articles, frequently asked questions, and product-specific instructions.

BM25 can rank these documents based on the terms used in a customer’s question.

For example, a user searching for “error 403 API authentication” may receive troubleshooting articles that contain the error code and related authentication terminology.

This can help users find answers more quickly and can support automated customer service workflows.

In more advanced systems, BM25 can retrieve candidate articles while a semantic model or reranker evaluates which results best address the user’s intent.

BM25 in Modern Hybrid Search Systems

Although BM25 remains effective for lexical retrieval, it does not inherently understand synonyms, paraphrases, or the broader meaning of a query.

Consider the query “how to make a website faster.” A relevant document titled “Improving web application performance” may use different words from the query and therefore receive a relatively low BM25 score.

Dense-vector retrieval can help identify such semantically related documents, even when they share few exact terms with the query.

Hybrid search combines the strengths of both approaches:

• BM25: Effective at matching exact terms, identifiers, and specialized vocabulary.
• Dense-vector retrieval: Effective at identifying semantic similarities and alternative phrasing.
• Reranking: Can evaluate a smaller set of candidate documents to improve their final ordering.

These methods are complementary rather than interchangeable. The best architecture depends on the query patterns, document collection, relevance requirements, and performance constraints.

BM25 Limitations and Best Practices

BM25 is a powerful and efficient ranking algorithm, but it is not a perfect solution for every search problem. Its ability to rank documents using term frequency, inverse document frequency, and document length normalization makes it highly effective for many keyword-based retrieval tasks. However, modern search applications often require more than matching words and measuring their statistical importance.

Understanding BM25’s limitations is essential for building search systems that deliver relevant results consistently. By combining appropriate preprocessing, parameter tuning, and retrieval evaluation, developers can improve BM25 performance and determine when additional techniques are needed.

Limitations of BM25

1. Limited Semantic Understanding

One of BM25’s biggest limitations is that it primarily relies on lexical matching. It evaluates the relationship between query terms and document terms but does not inherently understand their meaning or the intent behind a query.

For example, consider the search query:

“How can I make my website faster?”

A document titled “Improving web application performance” may provide an excellent answer, even though it does not use the exact words in the query.

Because BM25 relies on matching terms, it may assign this document a relatively low score if the wording differs significantly.

This limitation becomes particularly important when users express the same concept in different ways, use synonyms, or ask questions using natural language.

Best practice: Consider combining BM25 with dense-vector retrieval, which uses embeddings to identify semantically similar documents even when they share few exact terms with the query. Hybrid retrieval can provide a stronger balance between lexical matching and semantic understanding.

2. Sensitivity to Tokenization and Text Preprocessing

BM25 depends on how documents and queries are tokenized and normalized. If text preprocessing is inconsistent, relevant matches may be missed.

For example, a document might contain the term “databases,” while a query uses “database.” A simple whitespace tokenizer treats these as different tokens.

Similarly, punctuation, compound words, spelling variations, and language-specific morphology can affect matching behavior.

BM25 does not automatically resolve all these differences. The quality of its results therefore depends partly on how text is prepared before indexing and searching.

Best practice: Use consistent tokenization and normalization for documents and queries. Depending on the application, this may include lowercasing, punctuation handling, stemming, lemmatization, or language-specific analyzers. Test these choices against real queries, since aggressive normalization can also remove meaningful distinctions.

3. Keyword Frequency Does Not Always Equal Relevance

BM25 uses term frequency as one signal of relevance, but a frequently mentioned term does not necessarily mean a document is useful.

For instance, an article might repeat the phrase “machine learning applications” many times without providing meaningful information about the topic.

BM25 reduces this problem through term frequency saturation, which limits the benefit of repeated occurrences. However, the algorithm cannot reliably distinguish useful discussion from irrelevant repetition based on meaning alone.

Likewise, a short document containing the query terms may score highly despite lacking the context needed to answer the user’s question.

Best practice: Evaluate retrieval quality using representative queries and relevance judgments. For applications requiring greater precision, consider reranking the top BM25 results with a model capable of assessing the relationship between the query and each candidate document.

4. Document Length Normalization May Not Suit Every Collection

BM25 adjusts term contributions based on document length relative to the collection average. This is useful when comparing documents of different sizes, but the adjustment is not universally optimal.

For example, a short product description may contain all the information needed to answer a query, while a long technical manual may contain many useful details across multiple sections.

Depending on the task, length normalization can either help or reduce retrieval quality. The default value of the normalization parameter may not be ideal for every document collection.

Best practice: Tune the BM25 parameters against your own data. Consider how document length relates to relevance in your particular application, and evaluate whether standard normalization settings improve ranking quality.

5. Limited Understanding of Query Intent

BM25 generally treats a query as a collection of terms to match against documents. It does not inherently distinguish between different meanings of a word or fully interpret the relationships between terms.

Consider the query “Python performance problems.” Depending on the user’s intent, they might be looking for profiling tools, optimization techniques, or explanations of slow code.

BM25 can retrieve documents containing those terms, but it cannot reliably determine which interpretation best satisfies the user’s underlying need.

Best practice: Where query intent matters, consider query expansion, query rewriting, semantic retrieval, or a reranking stage. These techniques can help a retrieval system identify more appropriate results than lexical matching alone.

Best Practices for Improving BM25 Performance

Understanding BM25’s limitations is only the first step. The following practices can help you build a more reliable retrieval system.

1. Tune the k_1 and b Parameters

BM25 has two important parameters that control its ranking behavior:

• k_1: Controls term frequency saturation. Higher values generally allow term frequency to influence scores over a wider range.
• b: Controls the strength of document length normalization. A value of 0 disables length normalization, while a value of 1 applies the full normalization specified by the standard formula.

Common starting values are approximately k_1=1.2 and b=0.75, although defaults vary between implementations.

These values should be treated as starting points rather than universal optima. For example, a product catalog containing short descriptions may behave differently from a collection of long technical documents.

Recommendation: Experiment with parameter values using a representative evaluation dataset. Measure ranking quality for each configuration and select the settings that best match your retrieval objectives.

2. Optimize Text Preprocessing

The way documents and queries are processed can significantly affect retrieval quality.

Useful preprocessing considerations include:

• Consistent lowercasing where appropriate.
• Reliable tokenization for the language and content type.
• Handling punctuation and special characters.
• Applying stemming or lemmatization when beneficial.
• Preserving important technical terms, product identifiers, and error codes.

For example, a search system for software documentation may need to preserve identifiers such as HTTP 403 , C++ , or PostgreSQL rather than treating their punctuation as irrelevant.

Recommendation: Design preprocessing rules around the actual content of your collection. Avoid assuming that more aggressive text normalization will always improve search results.

3. Evaluate Ranking Quality with Appropriate Metrics

A search system should not be considered effective simply because it produces a ranked list. You need to determine whether relevant documents actually appear near the top.

Several evaluation metrics are useful for BM25-based retrieval:

Metric	What it measures	When it’s useful
Recall@k	The proportion of relevant documents retrieved within the top k results	When missing relevant information is costly
MRR (Mean Reciprocal Rank)	How highly the first relevant result appears	When users mainly need one good answer
nDCG (Normalized Discounted Cumulative Gain)	Ranking quality, accounting for relevance grades and result positions	When results have different levels of relevance
Precision@k	The proportion of the top k results that are relevant	When the top results need to be highly accurate

For example, if a search system returns useful documents but consistently places them below irrelevant results, its recall may be acceptable while its ranking quality remains poor.

Recommendation: Build a test set of realistic queries with human-assessed relevance judgments. Use it to compare parameter settings, preprocessing strategies, and alternative retrieval methods.

4. Consider Hybrid Retrieval

BM25 is strong at matching exact terms, but it may miss documents that express the same concept using different wording.

Hybrid retrieval combines BM25 with dense-vector search to address this limitation.

For example, a query for “car repair” may match a document containing those exact terms through BM25, while vector search may also identify a relevant document about “automotive maintenance.”

The two retrieval methods can produce complementary candidate lists. A system can combine their rankings or scores, often using a method such as Reciprocal Rank Fusion (RRF), to produce a unified result list.

Recommendation: Consider hybrid retrieval when users frequently use synonyms, paraphrases, or natural-language questions. Evaluate whether the added complexity improves results enough to justify the additional computation and operational cost.

5. Use Reranking When Higher Precision Is Needed

Another way to improve BM25 is to use it as the first stage in a multi-stage retrieval pipeline.

In this architecture, BM25 retrieves a relatively small set of candidate documents. A more sophisticated reranking model then evaluates those candidates against the query and reorders them.

A cross-encoder reranker, for example, can assess a query and candidate document together, allowing it to model their relationship more directly than a basic lexical ranking function.

A typical pipeline looks like this:

This approach can improve ranking quality without requiring an expensive model to evaluate every document in the collection.

However, reranking cannot recover relevant documents that the initial retrieval stage failed to retrieve. Candidate recall therefore remains important.

Recommendation: Use reranking when the quality of the top results matters enough to justify additional latency and computational cost. Evaluate the complete pipeline rather than assessing the reranker in isolation.

6. Choose the Right Search Architecture for Your Data

Not every application needs hybrid retrieval, neural reranking, or extensive preprocessing. A well-configured BM25 implementation may already provide excellent results for collections with precise terminology and queries that closely match document wording.

For example:

• Technical documentation: BM25 may be particularly effective for exact error codes, function names, and configuration settings.
• E-commerce: BM25 can provide lexical matching, supplemented by field weighting and business-specific ranking signals.
• Knowledge bases: BM25 may work well on its own or alongside semantic retrieval, depending on how users phrase questions.
• RAG systems: Hybrid retrieval and reranking may help when queries are conversational or relevant passages use different terminology.

Recommendation: Start with a simple BM25 baseline, measure its performance, and introduce more advanced techniques only when evaluation reveals a meaningful gap.

A Practical Strategy for Building Better Search

For most applications, an effective development process follows a few steps:

1. Establish a baseline: Implement BM25 with sensible default parameters.
2. Collect representative queries: Include common searches, difficult queries, synonyms, and domain-specific terms.
3. Measure relevance: Use suitable metrics and human judgments to identify failure cases.
4. Improve preprocessing and tuning: Test changes individually so you can understand their impact.
5. Evaluate advanced methods: Compare BM25 with hybrid retrieval or reranking when the baseline falls short.
6. Monitor real-world performance: Review unsuccessful searches and user feedback as the document collection and query patterns evolve.

This process helps avoid adding complexity without evidence that it will improve the search experience.

The Future of BM25: Hybrid and AI-Powered Search

As search technology evolves, the way we retrieve information is changing. Traditional keyword-based search remains valuable, but users increasingly expect search systems to understand natural-language questions, recognize related concepts, and deliver contextually relevant results. This shift has driven the adoption of semantic search, vector databases, and AI-powered retrieval techniques.

Despite these advances, BM25 remains an important part of modern search architectures. Rather than replacing it entirely, many systems combine BM25 with newer retrieval methods to take advantage of the strengths of both approaches.

The Rise of Hybrid Search

Hybrid search combines lexical retrieval, such as BM25, with semantic retrieval based on vector embeddings. The goal is to retrieve relevant documents using both exact keyword matches and conceptual similarity.

BM25 excels when a query contains specific terms, product names, technical identifiers, or phrases that appear directly in a document. Semantic search, on the other hand, can identify documents that express similar ideas using different wording.

For example, consider the query:

“How can I reduce my website’s loading time?”

A BM25-based system may prioritize documents containing terms such as “website,” “loading,” and “time.” A semantic retrieval system may also identify a document titled “Techniques for improving web performance,” even if it shares relatively few exact terms with the query.

By combining the two approaches, hybrid search can improve retrieval across a wider variety of queries.

A typical hybrid search pipeline follows these steps:

1. Receive a query: The user submits a search request.
2. Run BM25 retrieval: The system finds documents with strong lexical matches.
3. Run semantic retrieval: An embedding model identifies documents with similar meaning.
4. Combine the results: A fusion method, such as Reciprocal Rank Fusion (RRF), merges the ranked lists.
5. Return the best candidates: The system presents the highest-ranked documents or passes them to a downstream component.

The exact implementation varies, but the principle is straightforward: use lexical and semantic signals together rather than relying exclusively on either one.

BM25 in Retrieval-Augmented Generation (RAG)

One of the most promising applications of BM25 is retrieval-augmented generation (RAG), a technique that connects large language models (LLMs) to external information sources.

In a RAG system, a retrieval component finds relevant documents or passages, and an LLM uses that retrieved context to generate a response.

BM25 can serve as the lexical retrieval stage, helping the system locate passages that contain the important terms in a user’s question.

For example, imagine an AI assistant that answers questions about internal software documentation. A user asks:

“Where is the API authentication timeout configured?”

BM25 can retrieve documentation containing terms such as “API,” “authentication,” and “timeout.” These precise keyword matches can be especially useful when searching technical manuals, configuration files, or troubleshooting guides.

However, users do not always phrase questions using the same terminology as the documentation. Hybrid retrieval can help find relevant passages that use alternative wording, while a reranking model can further improve the ordering of retrieved candidates.

In practice, BM25 remains a valuable retrieval baseline for RAG because it is efficient, interpretable, and particularly effective at matching specific terminology. Its role is often complementary to semantic retrieval rather than competitive with it.

AI-Powered Reranking and Query Understanding

Beyond hybrid retrieval, AI models can improve search quality by interpreting queries and evaluating candidate documents more deeply.

Two approaches are particularly relevant.

Query expansion and rewriting

An AI model can rewrite a query or generate additional search terms to help retrieve relevant documents. For example, a search for “laptop battery life” might be expanded with related concepts such as “battery endurance” or “power consumption.”

These expanded terms can then be used in BM25 retrieval, semantic search, or both. However, poorly chosen expansions can introduce irrelevant matches, so they should be evaluated carefully.

Neural reranking

A reranking model can examine the query and each candidate document together to estimate their relevance more directly. Instead of relying primarily on term statistics, it can use contextual information to distinguish documents that genuinely address the query from those that merely contain similar words.

A common architecture uses BM25 to retrieve an initial set of candidates, followed by a neural reranker that reorders them. This approach limits the more expensive model’s workload while improving the quality of the final ranking when the reranker is effective.

These techniques allow search systems to combine BM25’s efficiency with the contextual capabilities of modern AI models.

Will AI Replace BM25?

Although AI-powered retrieval continues to advance, BM25 is unlikely to become irrelevant in the near future.

There are several reasons it remains useful:

• Efficiency: BM25 can score lexical matches without running a neural model for every document.
• Exact matching: It works well for identifiers, product names, error codes, and specialized terminology.
• Interpretability: Its scoring factors are relatively straightforward to understand and tune.
• Complementarity: It can improve hybrid retrieval pipelines by supplying candidates that semantic retrieval might miss.

At the same time, BM25 has clear limitations. It does not inherently understand synonyms, context, or the intent behind a query. For conversational search and complex information needs, semantic retrieval and neural reranking can provide important advantages.

The most effective architecture depends on the application, dataset, latency requirements, and evaluation results. Some search systems may work extremely well with BM25 alone, while others benefit significantly from hybrid retrieval and AI-powered ranking.

Rather than viewing these techniques as replacements for one another, it is more useful to understand how they complement each other.

Conclusion

BM25 remains one of the most important algorithms in information retrieval, providing an efficient and effective way to rank documents according to their relevance to a search query. By combining term frequency, inverse document frequency, and document length normalization, it goes beyond simple keyword matching to produce more meaningful search results.

Throughout this article, we’ve explored how BM25 works, examined its scoring formula, compared it with TF-IDF, and implemented a practical search system in Python. We’ve also seen how it powers applications ranging from enterprise search and e-commerce to retrieval-augmented generation (RAG).

Despite its strengths, BM25 has limitations. It primarily relies on lexical matching and cannot inherently understand synonyms, context, or user intent. Techniques such as semantic search, hybrid retrieval, and neural reranking can help address these challenges and improve search relevance when needed.

For developers building search systems, BM25 is an excellent starting point. It is relatively simple to implement, computationally efficient, and provides a strong baseline against which more advanced retrieval methods can be evaluated.

As search technology continues to evolve, BM25 is likely to remain a valuable component of modern retrieval pipelines—not necessarily as a standalone solution, but as part of a broader approach that combines keyword matching with semantic understanding.

The best way to get started is to implement BM25 on your own dataset, evaluate its results using realistic queries, and experiment with different configurations. Once you understand its strengths and limitations, you’ll be better equipped to build search systems that deliver relevant information efficiently and reliably.