LLMs and RAG for Developers
How LLMs process text, how to prompt them, how to ground them in your own data with RAG, where RAG fails, and what makes an agent different.
5 parts · about 32 minutes of reading
- Part 1How ChatGPT Understands Your Questions: A Deep Dive into LLMs, Tokenization, and Transformers
Deep dive into how ChatGPT works: tokenization, Transformer architecture, self-attention, and sampling — with real Python code examples and diagrams.
Jul 22, 2026 · 13 min read - Part 2Mastering Prompting Techniques: From Zero-Shot to Persona-Based
Learn prompting techniques like zero-shot, one-shot, chain of thought, self-consistency, and persona-based with easy examples.
Sep 22, 2025 · 2 min read - Part 3Mastering RAG (Retrieval-Augmented Generation): A Step-by-Step Guide for Developers
Learn RAG (Retrieval-Augmented Generation) step by step—chunking, embeddings, vector databases, query retrieval, and LLM integration.
Oct 1, 2025 · 3 min read - Part 4Where RAG Fails: A Deep Dive Into the Real Limitations of Retrieval-Augmented Generation
RAG improves LLM answers but does not guarantee correctness. The real failure modes: poor retrieval, bad chunking, hallucination and stale data.
Jul 18, 2026 · 12 min read - Part 5Agents vs LLM: Why Brains Alone Aren’t Enough
The difference between an LLM and an AI agent: how agents add tools, memory and actions to a language model, with a small agent that fetches news and movies.
Sep 28, 2025 · 2 min read