What RAG solves — and what it does not
Large language models know language patterns, not your private business facts. RAG connects models to your documents, databases, and tickets at query time so answers cite current, company-specific information instead of plausible guesses.
RAG is not fine-tuning. Fine-tuning changes model behavior; RAG changes model context. Most enterprise knowledge applications need RAG first because content changes weekly — policies, pricing, product docs, support macros.
RAG also does not replace agents. Retrieval feeds context; agents decide actions. Together they power support copilots, internal search, sales enablement, and compliance Q&A.
Architecture components
A production RAG stack includes: ingestion pipelines (parse PDFs, HTML, Notion, Confluence), chunking and embedding, vector store (Pinecone, Weaviate, pgvector, Qdrant), retriever with filters, reranker optional but valuable, and generation with citation requirements.
Separate ingestion from query paths. Ingestion runs async on schedule or webhook; queries stay low-latency with cached embeddings and pre-warmed indexes.
Use metadata filters aggressively — product line, region, language, document type, effective date. Retrieval without filters returns semantically similar but wrong answers.
Chunking strategy that affects accuracy
Fixed token chunks are a baseline, not the finish line. Structure-aware chunking respects headings, tables, and lists. Parent-child chunking stores small chunks for retrieval and larger parents for generation context.
Overlap helps continuity but increases storage cost. Test 10–20% overlap on procedural docs; reduce overlap on FAQ-style content.
Re-chunk when documents change. Stale chunks are a top cause of RAG hallucination — the model retrieves outdated policy and states it confidently.
Hybrid search and reranking
Pure vector search misses exact matches — SKUs, error codes, legal clause numbers. Combine keyword search (BM25) with vectors and merge results. Most enterprise RAG systems use hybrid retrieval.
Rerankers cross-encode query and candidate chunks for sharper ordering. The extra latency is worth it for high-stakes answers — legal, medical, financial.
Tune top-k and score thresholds per use case. Support bots may need k=8; executive briefings may need k=3 with higher precision.
Evaluation and hallucination control
Build a golden dataset of question-answer pairs from real user queries. Measure retrieval recall@k, answer faithfulness, and citation accuracy. Automate regression tests on every index update.
Prompt models to answer only from provided context and say I don't know when evidence is insufficient. Combine with post-generation checks that verify claims against retrieved chunks.
Human review samples weekly. RAG quality drifts as content and models change — continuous evaluation is not optional at enterprise scale.
CODIZAM RAG delivery
We design RAG platforms with LangChain, LangGraph, FastAPI, and your choice of vector store — often pgvector for teams already on PostgreSQL. Ingestion pipelines handle messy enterprise formats, not just clean markdown.
We ship admin tools for reindexing, document approval, and analytics on query failures. Your team owns content; we make retrieval production-grade.
Whether you need internal copilot search, customer-facing help centers, or agent memory layers, CODIZAM builds RAG that stays accurate as your business evolves.