Embeddings and vector search are the foundation of many modern AI applications. Understanding the concrete use cases helps you recognize when this technology applies and how to configure it correctly for each scenario.
Search by meaning, not keywords. A user asks "how do I cancel my subscription" and gets results about "ending your membership" and "stopping your plan" — content with no shared words but the same meaning.
Chunk and embed the knowledge base. At query time, embed the query and retrieve the most similar chunks by cosine similarity.
The dominant pattern for building LLM applications over private or specialized knowledge. Embed a knowledge base, retrieve relevant chunks at query time, and include them in the LLM's context window as grounding.
RAG solves the key LLM limitation: training data is frozen at a cutoff date and never includes private enterprise knowledge. RAG provides up-to-date, domain-specific access without fine-tuning.
Users ask questions about specific documents — contracts, papers, reports, manuals. Chunks must be small and precise; the exact passage answering the question must be retrieved.
Requires careful chunk sizing and often hybrid search for high precision.
Suggest items similar to what a user engaged with — articles, products, videos. Similar-meaning items are found even without shared metadata or keywords.
Often works better with document-level embeddings than chunk-level.
Similar product search — E-commerce "shop the look" and "customers also viewed" features. Embed product descriptions or images and find nearest neighbors in the catalog.
Duplicate detection — Find near-duplicate support tickets, articles, or reviews by clustering embeddings or finding high-similarity pairs. Essential for deduplication and content moderation.
Anomaly detection — Documents far from all existing clusters are outliers. Identifies novel complaints, unusual code patterns, or security events that don't match known attack signatures.
Q&A needs small, precise chunks and hybrid search. Recommendations may need document-level embeddings. Any system with multiple users or products needs metadata filtering. Always design your chunking, model selection, and retrieval strategy around the specific use case.
Ask the AI assistant about any of these use cases — RAG, semantic search, recommendations, or duplicate detection.