Building RAG systems over technical documentation
Table of Contents
Table of Contents
Technical teams sit on large document sets: manuals, SOPs, API specs, incident runbooks. Retrieval-augmented generation (RAG) lets a language model answer questions only with evidence drawn from those documents—reducing hallucinations compared with raw prompting.
This note summarizes a practical pattern I use when shipping assistants over technical or operational documentation, including options for local or private LLMs. If you need help implementing this end to end, see Services. Related research on retrieval and hallucination filtering is in Publications.
Why RAG for technical docs
- Grounding: answers cite passages from your corpus, not only model memory.
- Freshness: update the index when docs change; no full model retrain.
- Privacy: keep embeddings and generation on-prem or in a private VPC when required.
Core pipeline
- Ingest Markdown/PDF/HTML into clean text; normalize headings and code blocks.
- Chunk by structure (section headings) rather than fixed character windows alone.
- Embed chunks with a domain-appropriate embedding model; store vectors in a vector DB or lightweight FAISS index for small corpora.
- Retrieve top-k passages for the user query (optionally hybrid: keyword + dense).
- Generate with a system prompt that forbids answering without retrieved context; return citations.
- Serve behind a FastAPI (or similar) API and wire UI or chat tools (e.g. n8n workflows).
Private / on-premise LLMs
When data cannot leave the organization:
- Run open weights (or licensed private models) on GPU servers.
- Keep the vector store on the same network.
- Log prompts and retrieved chunks for audit without shipping them to public APIs.
This matches the Conversational AI & RAG systems offering on this site.
Evaluation (do not skip)
- Golden Q&A set from real operators.
- Check citation faithfulness (answer supported by retrieved text).
- Track refusal quality when the corpus has no answer.
- For research-style hallucination span work, see the SemEval-2025 paper under Publications.
What to ship first
Start with a single high-value corpus (one product manual or SOP folder), a small evaluation set, and a FastAPI endpoint. Expand indexes and automation (ETL, n8n) after the first vertical works.
Next step: browse Services or Contact if you want help designing a RAG stack for your documentation.
