Learn about LLMs, LangChain, RAG, agents, and building AI-powered applications — explained in simple terms for everyone.

Recommended Learning Path:

1. Part 1 - LangChain Foundations: Understanding LLMs and Orchestration — What an LLM is, why orchestration frameworks exist, and the core LangChain building blocks.
2. Part 2 - LangChain Essentials: Building Blocks of AI Applications — Prompts, chains, and output parsers used in real applications.
3. Part 3 - Tool Calling: Giving Your AI Real-World Superpowers — Letting an LLM call functions and APIs instead of just generating text.
4. Part 4 - RAG Basics: Teaching AI About Your Documents — Retrieval-augmented generation: embeddings, vector stores, and grounding answers in your own data.
5. Part 5 - LangGraph & Agents: Building Intelligent Workflows — Multi-step, stateful AI workflows and agent loops with LangGraph.
6. Part 6 - Project: Build Your Own AI Agent — A hands-on project putting the previous five parts together.
7. Part 7 - Observability with LangSmith: Debug and Monitor Your AI — Tracing, debugging, and monitoring LLM applications in production.

Opinion, case studies, and explainers:

• Saturday Morning Coffee ☕ Thoughts on Agentic AI — Informal reflections on where agentic AI is heading.
• Case Study: Amazon Rufus - AI Shopping Assistant Done Right — What Amazon’s Rufus gets right about AI-assisted shopping.
• Skills vs MCP vs Agents: The Future of AI (Explained Simply) — Untangling three related but distinct ideas in plain language.

Related Computer Vision topic:

• Image Processing Fundamentals — Pixels, colour models, kernels, convolution, and feature extraction. Now the first entry in the Computer Vision series, since it’s foundational to vision rather than generative AI specifically.

What You’ll Learn:

• What large language models are and how orchestration frameworks like LangChain fit around them
• How to build prompts, chains, and structured output parsers
• How to give an LLM tools and let it call real functions and APIs
• How retrieval-augmented generation (RAG) grounds answers in your own documents
• How to build multi-step, stateful agent workflows with LangGraph
• How to trace, debug, and monitor LLM applications in production
• How these ideas connect to real products and the broader direction of agentic AI