Retrieval-augmented generation (RAG) exists to solve one problem: language models don't know your data, and retraining one every time your documents change isn't realistic. RAG instead retrieves relevant text at query time and hands it to the model as context.
The minimum viable pipeline
- Ingest your documents — split them into chunks small enough to retrieve precisely, large enough to keep context.
- Embed each chunk into a vector and store it in a vector database.
- Retrieve the chunks most similar to a user's query.
- Generate an answer, with the retrieved chunks in the prompt.
Where beginners stop, and where production starts
That four-step pipeline works in a demo and falls apart on real questions. Production RAG needs hybrid retrieval (vector search alone misses exact terms like product names), reranking (a second, more precise pass over the top candidates), and a confidence threshold that refuses to answer when nothing retrieved is actually relevant. I cover the last one in detail in why my RAG platform says "I don't know".
The one mistake that wastes the most time
Building the pipeline before building an evaluation set. Without a fixed set of question-answer pairs with known correct sources, every change you make is a guess dressed up as an improvement.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
