Semantic Search
Fast Take: Semantic search finds results based on meaning and intent, not exact word matches.
Layer: Retrieval Status: Mature Last Updated: 2026-01-06
Decision Box
✅ Use this when:
- Keyword search returns “close but wrong” results
- Users phrase questions differently
- You’re searching unstructured text
- You’re building RAG or AI assistants
❌ Ignore this when:
- Exact matches are required (IDs, SKUs)
- Your data is strictly structured
- Keyword rules already solve the problem
⚠️ Risk if misused:
- Poor embeddings = irrelevant results
- No metadata filters = noisy retrieval
- Over-retrieval without re-ranking
- Users can’t tell why something matched
Simple Explanation
⚠️ What it is:
Semantic search matches ideas, not words.
Analogy:
It’s like asking a librarian what you meant, not what you said.
Why it matters:
Most users don’t know the “right keywords.” Semantic search removes that burden.
Technical Breakdown
Key Concepts:
- Embedding models
- Vector databases
- Similarity metrics
- Metadata filters
- Optional re-ranking
Implementation Snapshot:
- Chunk size (tokens or characters)
- Overlap size
- Structure awareness (headings, paragraphs)
- Metadata attachment
Common Failure Modes:
- Treating semantic search like keyword search
- No explanation layer (low trust)
- No fallback for exact matches
- Ignoring hybrid approaches
Cost Reality:
- Cost profile: Medium
- Drivers: query volume + vector DB performance
Top Players
Company / Tool – why it matters here:
- Pinecone
- Weaviate
- Elasticsearch (hybrid)
- OpenSearch
- Vespa
Go Deeper
Appears in:
AI Foundations for Builders — Module 2: The Library
This concept is covered in Module 2 – The Library (RAG & Vector Databases)
Term Flow
Prerequisites:
- Embeddings
- Vector Databases
- Chunking
Next Concepts:
- Top-K Retrieval
- Re-ranking
- Hybrid Search
Often Confused With:
- Keyword search
- Full-text search
- Boolean search