When to Use Which Search Mode: Vector, Keyword, Hybrid, or Grep

Production RAG systems rarely fit neatly into a single retrieval strategy. A natural language question about a product concept calls for something different than a search for a specific error code or a compliance audit for a known document ID. Gateco supports four retrieval modes — vector, keyword, hybrid, and grep — each optimized for a different query shape. Choosing the right one improves both result quality and latency.

Vector search uses embedding similarity to find semantically related content. It excels at natural language queries where the user is expressing a concept rather than a specific term: "what does our policy say about contractor data access?" returns relevant paragraphs even if they never use those exact words. Vector search is the default starting point for most RAG use cases and powers the vast majority of AI assistant queries.

Keyword search applies ranked full-text retrieval (BM25) to surface content that shares specific terms with the query. It performs best when users know the vocabulary of the domain — searching for "ABAC policy evaluation" as a topic, or finding documents that contain a particular product name or regulation number. If your users are power users who know what they're looking for, keyword search often returns tighter, more predictable results than vector search.

Hybrid search fuses vector and keyword rankings into a single result set. A configurable alpha parameter controls the balance between semantic similarity and term relevance: alpha of 1.0 is pure vector, 0.0 is pure keyword, and 0.5 gives equal weight to both. Hybrid has become the industry standard for production RAG pipelines because it captures the strengths of both approaches — handling natural language queries while still surfacing exact term matches. If you're unsure which mode to use, hybrid is a safe default.

Grep search is deterministic exact-pattern matching with regex support. It does not use embeddings or relevance ranking at all — it scans indexed text for literal or regex matches and returns all documents that match. This is the right tool for operational queries: finding all log entries containing a specific error code, retrieving documents with a known identifier, or running a compliance audit for documents that reference a particular regulation string. Grep results are binary (match or no match), reproducible, and do not depend on embedding quality.

A practical decision framework: start with vector for open-ended natural language questions. Switch to keyword when your users know the domain vocabulary and expect term-precise results. Use hybrid as your default for mixed query populations — it degrades gracefully in both directions. Reach for grep when you need deterministic matches, audit reproducibility, or you're searching for specific identifiers and error codes where semantic drift would introduce noise.

Regardless of which search mode you choose, Gateco's deny-by-default policy enforcement applies uniformly. Every retrieval — vector, keyword, hybrid, or grep — is evaluated against your active RBAC and ABAC policies before results are returned. A principal without authorization to a resource will never see it, no matter which retrieval path was used. Switching modes changes how results are ranked, not whether your security posture holds.

One current boundary worth noting: Gateco's Grounded Answers feature (available on Pro and Enterprise plans) supports vector, keyword, and hybrid modes. Grep is excluded from answer synthesis because exact-match results may lack the semantic coherence needed for LLM context — the model performs better when the retrieved chunks are topically related to the question rather than simply containing a matched string. For grep use cases that need summarization, the recommended pattern is to grep first and then pass the matched documents through a separate vector retrieval step.