𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: 𝗧𝗵𝗲 𝗨𝗻𝗰𝗹𝗲-𝗡𝗲𝗽𝗵𝗲𝘄 𝗚𝘂𝗶𝗱𝗲
Stop asking AI to guess. Start giving it facts.
Most people think AI knows everything. It does not. It only knows what it learned during training. If you ask it about your private company data, it will hallucinate. It will lie to you with confidence.
Retrieval-Augmented Generation (RAG) fixes this.
Instead of asking an AI to answer from memory, you follow three steps:
• Retrieval: Find the right documents. • Augmentation: Add those documents to your prompt. • Generation: Let the AI answer based only on those documents.
To build a production-grade RAG system, you need more than just a simple script. You need engineering.
Here is the blueprint for a reliable system:
Data Preparation Do not embed entire documents. Break them into chunks. Use a sliding window approach with 1000-1500 tokens and a 200-token overlap. This keeps context intact.
The Storage Stack Avoid complex new infrastructure. Use PostgreSQL with the pgvector extension. It allows you to store your data and your vector embeddings in one reliable place.
Hybrid Search Vector search is great for concepts but bad for exact facts. Combine vector search with keyword search. This gives you both semantic meaning and exact precision.
Reranking Vector search is fast but can be noisy. Use a two-stage process. Use a fast model to find the top 20 results, then use a more accurate reranker to pick the best 5.
Preventing Hallucinations Use these five layers of protection: • Set strict retrieval boundaries in your prompt. • Use structured JSON output. • Validate that the AI actually used the provided evidence. • Implement confidence gating. • Force the AI to provide citations.
RAG is not magic. It is engineering. It is about clear data, proven patterns, and constant measurement.
Build systems that provide evidence, not guesses.
Source: https://dev.to/surajrkhonde/rag-pipeline-the-uncle-nephew-complete-learning-guide-7h4
Optional learning community: https://t.me/GyaanSetuAi