RAG Knowledge Base
A retrieval-augmented generation indexer and search layer running over internal engineering wikis, product requirement documents and core code repositories.
Onboarding answers without pulling in senior engineers
The problem
The reasoning behind a system rarely lives in the code. It sits in wikis, PRDs, and the heads of the three people who were in the room. New engineers burned days finding out why something was built a particular way, and the people who knew were the ones being interrupted.
How it works
- Automated markdown parsing and ingestion pipelines over wikis, PRDs and repositories, re-run as sources change.
- Vector embeddings generated through the OpenAI API, retrieved by cosine similarity.
- Fast semantic lookup against a vector index (Pinecone/Chroma) tuned for the corpus size.
- A contextual prompt-assembly layer that returns architectural references and onboarding answers with their sources attached, so answers can be checked.
What changed
Onboarding lookups moved from "ask a senior engineer" to "ask the knowledge base and verify the citation." Keeping source attribution in every answer mattered more than raw retrieval quality. An unsourced answer about your own architecture is not something a team will act on.