[Ch 5] RAG Integration — Giving Your Agent Real Knowledge
Turn the stub tools from Ch 4 into real implementations: embed project documentation with OpenAI embeddings, build a FAISS index, implement search_docs with vector similarity …
Turn the stub tools from Ch 4 into real implementations: embed project documentation with OpenAI embeddings, build a FAISS index, implement search_docs with vector similarity …
A hands-on series covering everything you need to build production-grade AI agents — from core concepts and LangGraph fundamentals to RAG integration, memory, guardrails, tracing, …
Build a complete, multi-turn AI agent from scratch using LangGraph — with persistent memory via SQLite checkpointer, proper streaming output, structured tool schemas, and …
A practical introduction to LangChain's core building blocks and LangGraph's stateful graph abstraction — including messages, @tool, StateGraph, nodes, edges, and a complete Hello …
What exactly is an AI agent, how does it differ from a chatbot or an LLM pipeline, and when should you actually use one? This chapter covers the agent loop, real use cases, and the …
A deep dive into the four core components of an AI agent system, and why Context Engineering — managing everything in the LLM's context window — matters far more than just writing …
Why I wrote this series, what you'll build, and how to follow along — an overview of all nine chapters covering the full lifecycle of a production AI agent.
A curated 18-month learning roadmap for becoming an AI Speech Engineer — covering foundations, core technologies (ASR, TTS, Speaker Verification, Diarization, Voice Conversion), …
A guide to writing technical content in Academic — highlighting code snippets, rendering math equations, and drawing diagrams from text.