Retrieval-augmented generation (RAG) applications combine document retrieval with large language models (LLMs) so answers are grounded in your data. This guide walks through the main stages teams follow when they build a RAG system from scratch—or when they adopt a framework like WeKnora that bundles these pieces.
Decide which documents users can query (internal wikis, PDFs, tickets, policies) and what “good” answers look like. Clarify languages, update frequency, and whether answers must include citations. This drives chunking, access control, and evaluation later.
Normalize files into text and structure: PDFs, Word, HTML, Markdown, and scanned pages (OCR when needed). Preserve headings, tables, and lists where possible so chunks stay meaningful. See PDF to knowledge base and Document AI for format-specific notes.
Split documents into segments sized for your embedding model and retrieval window. Add overlap between chunks to avoid cutting sentences in half. Structure-aware splitting (by section or heading) often beats fixed character counts. Details: RAG chunking best practices.
Turn each chunk into a vector with an embedding model, then store vectors and metadata (source id, page, permissions) in a vector store. Choose an embedding family that matches your languages and domain. More: Embeddings for RAG and Semantic search & vector search.
At query time, embed the user question, run nearest-neighbor search, and optionally rerank candidates with a cross-encoder or LLM to improve precision. Tune top-k and score thresholds for your use case.
Pass retrieved chunks as context to the LLM with a clear system prompt: answer only from context, cite sources, and refuse when evidence is missing. This is the core of RAG versus raw chat. Product patterns: Chat with your documents.
Measure retrieval hit rate, answer faithfulness, and latency. Add logging for failed queries and expand synonyms or chunking where retrieval misses. For production and compliance, review Enterprise RAG & security.
WeKnora provides document understanding, vector indexing, semantic retrieval, and LLM integration in one open-source stack so you can focus on product rather than wiring every RAG component yourself.