*Stanford dropped the best 1-hour lecture on building AI agents* Here's a 28-point summary: 1/ Chain-of-thought prompting works because it slows down the model’s reasoning. Slower = smarter. 2/ Prompting is the new programming. You don’t need to write code. You need to write better instructions. 3/ A good prompt can make a weak model feel smart. A bad prompt can make GPT-4 feel dumb. 4/ Teaching AI is like teaching a junior teammate. Be clear, show examples, and ask to explain before answering. 5/ To get better AI answers, break your task into smaller steps. Feed it one step at a time. 6/ "Explain your reasoning first, then answer," works better than just "answering this." 7/ Your prompt is your product. Most users don’t need more model power, they need clearer instructions. 8/ Don't just say "summarize this." Say, "Summarize this in 5 bullets for a busy CEO." 9/ The more examples you show in your prompt, the more consistent the AI becomes. Think: few-shot prompting. 10/ Retrieval-Augmented Generation (RAG) is how you stop AI from making things up. Give it facts to pull from. 11/ RAG lets you feed your own data into the AI, like a private brain you can query. 12/ RAG works by turning your docs into searchable blocks that the AI can pull from when needed. Simple but powerful. 13/ Prompt engineering isn't a trick. It's a system. Like UX, but for thoughts. 14/ Use logging and tracing to debug prompts. If the answer is wrong, trace the thinking. 15/ You don’t always need to fine-tune a model. Just tune the prompt. 16/ Even 10 examples of great input/output can steer the model better than 1000 lines of code. 17/ Agentic AI = giving LLMs the ability to reason, act, reflect, and repeat. 18/ Reflection patterns help AI critique its own output and improve it in the next round. Yes, like self-review. 19/To get AI to fix a bug, ask it to write the fix. Then ask it to review its own fix before shipping. 20/ Agents are just smart loops: plan → act → observe → reflect → repeat. 21/ Multi-agent systems break big tasks into smaller ones, like a team, but all bots. 22/ Different agents can play different roles. One plans, one writes, one checks. Each with a unique prompt. 23/ Most hallucinations happen because the model is guessing. Prompt it with context so it doesn’t have to. 24/ Guardrails matter. Use smaller models to check if the big one is going off the rails. 25/ AI that can call APIs or run code is way more useful than just chatting. 26/ LLMs can now browse, fetch data, and even file pull requests. It’s not just chat anymore, it’s action. 27/ The best AI workflows combine prompting + memory + tools + feedback loops. 28/ Final insight: Stop chasing new models. Start mastering the one you already have with better prompts. Share this with the next dev who asks what “ *agentic AI* ” even means. https://youtu.be/kJLiOGle3Lw?si=M7uBcUX1EJydb5bxP