BM25 has been the backbone of search for decades and remains essential in the AI era. While neural methods get the headlines, BM25 often handles the first retrieval stage in hybrid AI systems. Understanding BM25 explains why keyword presence still matters even in semantic search, and why traditional SEO fundamentals remain relevant for AI visibility.
How BM25 Works
- Term Frequency (TF): Documents with more occurrences of query terms score higher, with diminishing returns.
- Inverse Document Frequency (IDF): Rare terms across the corpus are weighted more heavily.
- Document Length Normalization: Longer documents don’t automatically win; length is normalized.
- Saturation: Term frequency impact saturates—10 mentions isn’t much better than 5.
BM25 Formula Components
| Component | What It Measures | Impact |
|---|---|---|
| TF (Term Frequency) | How often term appears in doc | Higher is better (with saturation) |
| IDF (Inverse Doc Freq) | How rare term is across corpus | Rare terms weighted higher |
| k1 parameter | TF saturation speed | Typically 1.2-2.0 |
| b parameter | Length normalization strength | Typically 0.75 |
Why BM25 Matters for AI-SEO
- First-Stage Retrieval: Many AI systems use BM25 to get initial candidates before neural reranking.
- Hybrid Systems: BM25 combined with dense retrieval is common; optimizing for both maximizes coverage.
- Exact Matching: Brand names, technical terms, and specific queries need BM25-style keyword matching.
- Baseline Performance: Strong BM25 performance ensures visibility in both traditional and AI search.
“BM25 is the workhorse of search. While neural methods add semantic understanding, BM25 ensures you’re found when someone searches for exactly what you offer.”
Optimizing for BM25
- Include Target Keywords: Ensure key terms appear in your content, especially in titles and early paragraphs.
- Natural Keyword Usage: Multiple mentions help, but saturation means you don’t need excessive repetition.
- Long-Tail Terms: Include specific, less common terms that have high IDF value.
- Appropriate Length: Cover topics thoroughly but avoid unnecessary padding.
Related Concepts
- Sparse Retrieval – The retrieval category BM25 belongs to
- Hybrid Search – Combining BM25 with dense retrieval
- TF-IDF – BM25’s predecessor algorithm
Frequently Asked Questions
Absolutely. Most production AI search systems use BM25 or similar algorithms as the first retrieval stage, often combined with neural reranking. BM25’s speed and precision for exact matches make it indispensable even in advanced AI pipelines.
BM25 matches keywords directly—if the exact term isn’t present, there’s no match. Neural search understands meaning, so “automobile” can match “car.” Both have strengths: BM25 for precision, neural for semantic understanding. Modern systems use both.
Sources
- The Probabilistic Relevance Framework: BM25 and Beyond – Robertson & Zaragoza
- Practical BM25 – Elasticsearch
Future Outlook
BM25 will remain relevant as hybrid search becomes standard. Learned sparse methods like SPLADE may eventually supplement BM25, but the principle of keyword matching will persist. Optimizing for both lexical and semantic retrieval is the winning strategy.