Case study / 2026

Documentation Assistant

RAG application that indexes technical documentation URLs and answers questions with retrieved context.

code

Python, FastAPI, LangChain, RAG

Documentation Assistant interface

Problem

Developer docs are long, versioned, and scattered. The goal was to make a support assistant that can answer with grounded context instead of hallucinating a confident paragraph.

System Shape

  1. 01User submits one or more documentation URLs.
  2. 02Backend crawls and chunks the pages into retrievable passages.
  3. 03Embeddings are stored with source metadata for later attribution.
  4. 04Query flow retrieves relevant chunks before sending the prompt to the model.
  5. 05Responses are returned with enough source context to debug the answer.

Highlights

  • -Built the indexing and chat loop as a full-stack RAG workflow.
  • -Separated retrieval context from answer generation so failures are easier to inspect.
  • -Focused the interface around technical support instead of general chat.

Lessons

  • -Chunk boundaries often matter more than the model choice.
  • -A useful answer needs provenance, especially when the source is technical documentation.
  • -The first version of a RAG system should make retrieval failures visible.

What I Would Do Next

Add evaluation sets for common documentation questions and compare retrieval strategies across chunk sizes, rerankers, and citation quality.