This introduction belongs to our open-source knowledge series. For implementation-level detail, read the RAG & retrieval platform best-practices guide next—it covers evaluation loops, hybrid retrieval, access control, and failure modes. Below we focus on decision framing: what executives and product leads should validate before scaling budget against model logos.
Why demos lie
Most “RAG demos” attach an LLM to a folder of PDFs and celebrate plausible answers. Production breaks where retrieval quality is unmanaged: stale chunks, wrong permissions, ambiguous tables mixed with prose, and no regression tests when documents change. Open source helps because you can inspect indexes, logs, and pipelines—but only if you instrument them.
Choose the data plane before you chase the model
Teams argue about Llama vs Mistral while the real leverage sits in chunking, metadata, hybrid retrieval (dense + lexical), and refresh strategy. Whether you lean on pgvector in Postgres, OpenSearch for hybrid patterns, or both, the architecture must match query shapes and ops capability. Copying a notebook stack without matching those realities ships tech debt.
Delivery paths we commonly tie together:
- RAG implementation (agency) — end-to-end retrieval products with evaluation duties.
- LlamaIndex consulting — document pipelines, structured retrieval, complex ingestion.
- PostgreSQL & AI backend — durable schemas, permissions, vector columns where appropriate.
- OpenSearch search platform — relevance tuning, hybrid search, operational search estates.
Evaluation is not optional
You need a minimal loop: benchmark questions, regression after content updates, and clarity on “good enough” for your risk domain (internal wiki vs regulated dossiers). Without it, quality erodes silently while leadership debates prompts.
Related introductions
For orchestration of agents on top of retrieval, see LLM agents & orchestration. For owning inference infrastructure rather than APIs-only, Self-hosted AI infrastructure.
Trademark notice
Named tools are for orientation only; owners retain trademarks. No endorsement implied.
Deploy RAG with explicit guardrails
We align retrieval quality, access control, and integration reality—then scale with evidence.
