2025 · Implementation

RAG Pipeline for Technical Documentation

Built a retrieval-augmented generation pipeline that indexes technical documentation, API references, and internal wikis to power accurate, citation-backed answers for developer support.

rag search embeddings

Overview

Developed a production RAG system for a developer tools company that needed to provide accurate, up-to-date answers to developer questions using their extensive documentation corpus.

Technical Approach

The pipeline processes multiple document formats (Markdown, OpenAPI specs, code examples) through a unified ingestion pipeline:

  1. Document parsing — custom parsers for each format, preserving code blocks and structure
  2. Chunking strategy — semantic chunking based on document structure rather than fixed token windows
  3. Embedding — hybrid approach using dense embeddings (for semantic search) and sparse embeddings (for keyword matching)
  4. Retrieval — reciprocal rank fusion combining both retrieval methods
  5. Generation — prompt engineering with citation formatting and hallucination guards

Infrastructure

  • Ingestion runs on a scheduled pipeline processing ~50K documents daily
  • Vector store (Qdrant) with real-time index updates
  • Latency budget: 200ms for retrieval, 2s total including generation
  • Monitoring for retrieval quality using automated evaluation datasets

Results

  • 85% of developer questions answered without human escalation
  • 94% citation accuracy (verified against source documents)
  • 3x reduction in average support ticket resolution time