AI Solutions
RAG Implementation Guide: From Business Documents to a Production Knowledge Assistant
A practical RAG implementation guide covering source preparation, permission-aware retrieval, citations, evaluation, prompt-injection risks, cost and production monitoring.
Retrieval-augmented generation (RAG) is useful when an AI assistant must answer from information that is specific to an organization, changes over time, or should not be treated as part of a model’s general knowledge. A prototype can look simple: retrieve a few passages and place them in a model prompt. A production knowledge assistant is harder because source quality, access control, evaluation, citations and failure behavior all matter. Start with representative questions Before selecting an embedding model or vector store, collect realistic questions from the intended users. For each question, record what a good answer should contain, which source should support it, whether more than one source is needed, what the assistant should do when evidence is missing, and whether users have different access rights. This creates an evaluation set before architecture decisions become difficult to change. Treat source quality as part of answer quality RAG cannot consistently produce trustworthy answers from contradictory, obsolete or poorly labeled source material. Review the collection for duplicate versions, missing ownership, weak PDF extraction, mixed countries/products and confidential documents stored beside broadly accessible material. Useful metadata can include a stable document ID, title, owner, business unit, country/region, version or effective date, access classification, section heading and ingestion timestamp. Chunk according to meaning Chunks that are too large mix topics and increase context cost; chunks that are too small lose definitions and exceptions. Prefer natural boundaries such as headings, clauses, FAQ pairs, documentation sections and procedural steps. Preserve enough context to understand what each passage refers to. Make permissions part of retrieval If a user cannot read a source, that source should not enter model context. Filtering the final answer after retrieval is too late. Permission-aware retrieval may use tenant IDs, user roles, team/project membership, country or legal entity, document access levels or explicit ACLs. Enforce these rules in a trusted backend/data layer. For applications using PostgreSQL or Supabase, test both allowed and denied retrieval cases. Authentication identifies the user; authorization decides what that user may retrieve. Retrieval quality is more than vector similarity Production retrieval may combine semantic search, keyword/full-text matching, metadata filters, recency/version rules, source priority and reranking. A semantically similar but obsolete policy can still be the wrong answer. Did the correct source enter the candidate set? Was it ranked high enough for model context? Did an obsolete or unauthorized source outrank it? If the right evidence is never retrieved, changing the answer prompt will not fix the underlying problem. Preserve citations as data Keep source identity attached to retrieved passages through the entire pipeline. Give the model explicit source identifiers, then resolve those identifiers in the UI to a document title, section, URL/file, excerpt and version/date where useful. This gives users a verifiable path from generated answer back to evidence. Assume retrieved documents are untrusted input Retrieved content can contain text that looks like instructions to a model. Separate application instructions from document text, keep tool permissions narrow, validate structured outputs and avoid high-impact automatic actions based only on retrieved content. Test adversarial documents as part of evaluation. Design for weak evidence A trustworthy assistant...