Designing a Production-Oriented RAG System for Technical Documentation
The article outlines the design of a production-grade Retrieval-Augmented Generation (RAG) system tailored for technical documentation at VizLab.xyz. The system prioritizes accurate retrieval from curated engineering sources to reduce hallucinations and improve reliability in developer support. It integrates tools like FastAPI, FAISS, BM25, AWS Bedrock, and Docker for a self-hosted, citation-aware architecture.
- ▪The RAG system was built to support internal technical documentation retrieval and developer assistance at VizLab.xyz.
- ▪It uses a retrieval-first architecture to minimize hallucinations by grounding LLM responses in trusted, curated documentation.
- ▪The pipeline includes offline ingestion, retrieval, and generation stages, with raw content stored in AWS S3 for durability and reproducibility.
- ▪Documentation sources include Docker, Terraform, NGINX, AWS, Solidity, and GitHub Actions, ensuring domain-specific accuracy.
- ▪The system employs text cleaning, BM25, FAISS, and AWS Titan Embeddings to enhance retrieval precision and contextual relevance.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3698384) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Prajwal Posted on May 17 Designing a Production-Oriented RAG System for Technical Documentation #ai #devops #opensource #programming Large Language Models are incredibly powerful, but they have a major limitation: They do not inherently know your infrastructure, your internal documentation, your deployment standards, or your engineering workflows.
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