Inside RAG - How It Really Works (Part 2/4)

Utkarsh Patel — Wed, 27 May 2026 04:48:48 +0000

RAG seems pretty straightforward at first glance. At the heart of it, the philosophy is, split your documents into chunks, search for the ones that match the question, feed them into the prompt, and finally, have the LLM generate an answer.

But the interesting parts live in the nitty gritty details of how that retrieval piece actually functions. There are four main stages: chunking, embedding, retrieval, and generation. Each one has far more depth and gotchas than the typical quick-start tutorial ever mentions. Let’s dive into the pipeline and look under the hood.

Inside RAG — How It Really Works (And Why Most Projects Stall Before They Ship) — Things With AI

The gap between the RAG concept and production reality is where most projects quietly fail. Here's what the explanations usually skip.

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RAG vs. Fine-Tuning - The Question Every AI Builder Gets Wrong (Part 1/4)

Utkarsh Patel — Wed, 27 May 2026 04:46:00 +0000

LLMs are trained on publicly available data and broad general knowledge. They’re remarkably capable across a wide range of tasks. However, they do not inherently understand the proprietary knowledge that defines how your company actually operates. Internal policies, architectural decisions, pricing structures, contract terms, issue tracking systems, and recent product changes all exist outside the model’s built-in knowledge. This is where commercial adoption often runs into real limitations. When customers ask company-specific questions, the model frequently lacks a reliable source of truth. In those moments, it does what it was designed to do: generate the most plausible answer based on patterns. Sometimes that answer is right. Often, it is not.

This is fundamentally a knowledge problem, not purely a model quality problem. And it is one that nearly every organization building customer-facing systems eventually encounters.

Take a common scenario: a customer asks your assistant, “What’s our refund policy for enterprise subscriptions?” The system responds immediately and confidently, but the answer is incorrect. That is where trust begins to break.

Two approaches have traditionally been the standard response: Fine-Tuning and Retrieval-Augmented Generation (RAG). These days there is also a third layer: Agentic RAG.

RAG vs. Fine-Tuning — The Question Every AI Builder Gets Wrong — Things With AI

AI models don't know your private data. Two approaches have been the standard answer. In 2026, a third matters just as much.

thingswithai.org

DEV Community: Utkarsh Patel

Inside RAG - How It Really Works (Part 2/4)

Inside RAG — How It Really Works (And Why Most Projects Stall Before They Ship) — Things With AI

RAG vs. Fine-Tuning - The Question Every AI Builder Gets Wrong (Part 1/4)

RAG vs. Fine-Tuning — The Question Every AI Builder Gets Wrong — Things With AI