Keyword Search to RAG Platform Blueprint

What and Why

Keyword search returns documents and leaves users to read them. Retrieval-augmented generation (RAG) retrieves the most relevant passages and has an LLM synthesize a grounded, cited answer. The model's knowledge stays current because answers are built from your retrieved content, not just its training data.

This blueprint builds a RAG platform: ingestion and chunking, embeddings into a vector store, hybrid retrieval, and a grounded generation layer with guardrails and evaluation.

Phases

Assessment. Define the corpus, access controls, and the questions users actually ask. Establish an eval set of question/answer pairs as the quality bar.

Ingestion and indexing. Build a pipeline that parses documents, chunks them sensibly (respecting structure), generates embeddings, and stores vectors plus metadata. Use pgvector or a dedicated vector database. Keep a re-index path for updated content.

Retrieval design. Combine semantic (vector) and keyword (BM25) search for hybrid retrieval, then re-rank. Apply metadata filters and per-user access control so retrieval respects permissions.

Generation and guardrails. Prompt the LLM with retrieved passages and require citations. Add guardrails: refuse when context is insufficient, defend against prompt injection in retrieved text, and limit context size for cost.

Evaluation and rollout. Score answers for relevance and groundedness with an eval harness (including LLM-as-judge). Roll out behind a flag, monitor, and iterate on chunking and retrieval.

Key Risks and Mitigations

• Hallucination: the model may answer beyond the context. Require citations, instruct it to say when it doesn't know, and measure groundedness in evals.
• Data freshness: stale vectors give wrong answers. Automate re-indexing on content change and store source timestamps.
• Prompt injection: retrieved documents can contain malicious instructions. Treat retrieved text as untrusted, sandbox tool use, and apply injection defenses.

Recommended Tooling

An embedding model, a vector store (pgvector on PostgreSQL or a dedicated vector DB), a keyword index (Elasticsearch) for hybrid search, a re-ranker, an LLM gateway with guardrails, Redis caching, and an eval harness with LLM-as-judge.

Success Metrics

Measure answer relevance and groundedness from evals, user deflection rate (questions resolved without escalation), retrieval recall, and cost per answer.

Prerequisites

A defined corpus with access metadata, an embedding and LLM provider, a vector store, and an eval dataset to guard against regressions.