One of the most common questions when building AI applications is:
Should I use BM25, Vector Search, Hybrid Search, SQL, or an LLM?
The answer is almost always:
It depends on what your users are trying to do.
Retrieval isn't a single problem.
Users have different intents, and each intent favors a different retrieval strategy.
Let's use a simple pizza menu to illustrate.
🍕 The Menu
- Margherita Pizza
- Pepperoni Special
- Veggie Supreme
- Cheese Lovers
Now imagine users searching in different ways.
Some know exactly what they want.
Others describe what they want.
Some ask questions.
Each requires a different retrieval strategy.
🎯 BM25: Exact Lookup
BM25 is traditional keyword search.
It excels when users already know what they're looking for.
Examples
- Pizza #3
- Margherita Pizza
- Order #12345
- Error Code 500
Strengths
- Fast
- Simple
- Highly precise
Limitation
BM25 understands words—not meaning.
Searching for "cheese pizza" won't necessarily find pizzas that only mention mozzarella or parmesan.
The good news is that BM25 is often enhanced with features such as:
- Fuzzy matching
- Prefix matching
- Stemming
- Synonym expansion
- Phonetic search
These improvements make keyword search much more forgiving, but they still don't provide true semantic understanding.
Best for
- IDs
- Product names
- Error codes
- Exact matches
🧠 Vector Search: Semantic Discovery
Vector search understands meaning rather than keywords.
Instead of matching words, it matches concepts.
For example:
"Something with cheese but no meat."
A vector search understands that mozzarella, parmesan, provolone, and cheddar are all forms of cheese.
Likewise,
"Vegetarian options"
finds pizzas without meat, even if the word vegetarian isn't explicitly present.
It also handles many spelling variations naturally.
"Margarita" → "Margherita"
Unlike BM25, these capabilities don't require manually defining synonyms or fuzzy rules.
Vector search is also commonly combined with metadata filtering to narrow results.
Best for
- Natural language
- Synonyms
- Concept search
- Discovery
⚖️ Hybrid Search: Best of Both
Real-world users don't all search the same way.
Some search:
Pizza #3
Others search:
Something spicy
Hybrid search combines BM25 with vector search.
BM25 provides precision for exact matches.
Vector search provides semantic understanding.
Together they produce better results than either approach alone.
For many production systems, hybrid search delivers the best overall user experience.
Best for
- Customer-facing search
- E-commerce
- Enterprise knowledge bases
- Mixed search behavior
🤖 LLM-Powered Retrieval
Sometimes retrieval isn't the problem.
Reasoning is.
Instead of querying an index directly, an LLM understands the user's request, expands it into multiple search strategies, executes those searches, and synthesizes the results.
For example:
"Find all places where authentication is implemented."
The model may search for:
- login
- authentication
- OAuth
- JWT
- identity
- authorization
and combine the results into a single answer.
This is similar to how tools like Claude Code search large codebases.
The trade-off is cost and latency.
Each query requires one or more LLM calls, making this approach significantly slower and more expensive than indexed retrieval.
Best for
- Developer tools
- Code exploration
- Internal documentation
- Research workflows
🗄️ Don't Forget SQL
Not every retrieval problem needs vectors.
If a user asks:
"Show all orders placed yesterday."
or
"List customers in California."
that's a structured query.
A relational database or metadata filter is often simpler, faster, and more accurate than semantic search.
One of the biggest mistakes in AI systems is using vector search where SQL is the better solution.
📋 Choosing the Right Retrieval Strategy
| Example Query | Best Approach | Why | Helpful Features |
|---|---|---|---|
| Pizza #3 | BM25 | Exact identifier lookup where precision matters most | Fuzzy search, prefix matching |
| Margherita Pizza | BM25 | User knows the exact name and expects an exact match | Synonyms, stemming |
| Vegetarian options | Vector Search | User is searching by concept rather than keywords | Metadata filtering |
| Something with cheese but no meat | Vector Search | Requires semantic understanding of ingredients and constraints | Metadata filtering |
| Margarita pizza | BM25 + Fuzzy or Vector Search | Needs typo tolerance while preserving relevance | Fuzzy matching or semantic similarity |
| Pizza under $15 with mushrooms | SQL + Vector Search | Price is structured data; description is unstructured | Structured filters + semantic search |
| Pizza #3 or recommend something similar | Hybrid Search | Combines exact lookup with semantic recommendations | BM25 + Vector fusion |
| Find all places where authentication is implemented | LLM-Powered Retrieval | Requires reasoning, query expansion, and synthesis across multiple sources | Multi-step reasoning |
⚠️ Every Approach Has Trade-offs
There isn't a perfect retrieval strategy.
BM25
- Doesn't understand meaning.
- Requires additional features like fuzzy search and synonym expansion for better recall.
Vector Search
- Understands semantics but may rank conceptually similar results above exact matches.
Hybrid Search
- Delivers excellent results but requires tuning and balancing.
LLM-Powered Retrieval
- Powerful reasoning but higher cost and latency.
SQL
- Excellent for structured data but poor for semantic discovery.
Understanding where each approach fails is just as important as understanding where it succeeds.
🎯 Final Thought
The question shouldn't be:
"Which retrieval technology is the best?"
The better question is:
"What kind of retrieval problem am I trying to solve?"
Sometimes the answer is BM25.
Sometimes it's vector search.
Sometimes it's SQL.
Sometimes it's an LLM.
And increasingly, the best AI applications combine multiple retrieval strategies, using the right tool for the right job rather than forcing every query through the same pipeline.
