Kirti Kumar Sahu · August 2, 2026 · 10:00 · 1 min read
Continuing from Part 1 — here's why RAG earns its popularity instead of just being another buzzword.
It cuts down hallucination. LLMs get confident even when they're wrong. Ask something outside their training and they'll often invent a plausible-sounding answer rather than admit they don't know. RAG doesn't eliminate this, but it grounds the answer in retrieved, real data instead of the model's memory, which makes fabrication a lot less likely.
It gets around the knowledge cutoff. Every model has a training cutoff, a date past which it knows nothing new. GPT-5's is September 30, 2024 — checked this while fact-checking my notes, and it holds up. RAG doesn't move that cutoff, it works around it: hand the model current information at query time and it doesn't matter that training stopped months or years ago.
It's cheaper than retraining. Teaching a model new information the hard way means fine-tuning or retraining — expensive, and something you'd have to repeat every time the data changes. RAG skips that entirely. You update the data source; the model gets the new information on the next query, no retraining involved.
It keeps sensitive data contained. This one matters most for companies. Nobody wants to fine-tune a model on internal data and hope it never leaks back out. With RAG, the model doesn't need to see the whole dataset — for any given query it only receives the specific chunks relevant to that query. The rest of the database stays untouched by the model entirely.
Next part: how this actually gets built, the ingestion and retrieval pipeline.