![[1hr Talk] Intro to Large Language Models](https://img.youtube.com/vi/zjkBMFhNj_g/hqdefault.jpg)
[1hr Talk] Intro to Large Language Models
Still the best one-hour mental model of what an LLM actually is before you build on one.
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Building & Product
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How do founders ship their first AI feature (APIs, wrappers, RAG in plain terms)?
Your first AI feature is usually one API call: send the model your instructions plus the user's input, get text or structured JSON back, and wrap it in your existing UI. RAG simply means fetching your own data (docs, tickets, listings) and pasting the relevant bits into the prompt so the model answers from your facts instead of its memory. Ship a thin version in days with the OpenAI or Claude API and a toolkit like Vercel's AI SDK, then iterate on prompts before touching anything fancier.
Go deeper
19 resources.
Deep Dive into LLMs like ChatGPT
Karpathy's updated general-audience walkthrough covering training, hallucinations, and tool use.
Watch on YouTube youtube.com →
RAG Explained For Beginners
Retrieval-augmented generation explained without jargon, the concept behind most useful first AI features.
Watch on YouTube youtube.com →
Build and Deploy a RAG Chatbot with JavaScript, LangChain.js, Next.js
A 90-minute end-to-end build you can follow along with, from empty repo to deployed chatbot.
Watch on YouTube youtube.com →How I AI with Claire Vo
30-minute episodes where real builders demo exactly how they shipped AI features, demos included.
Listen on Apple Podcasts podcasts.apple.com →Latent Space: The AI Engineer Podcast
The default podcast of people who ship AI features for a living; subscribe and skim show notes.
Open latent.space →Learn RAG from Scratch (Python tutorial from a LangChain engineer)
Free, thorough, and taught by the engineer who wrote much of LangChain's RAG tooling.
Open freecodecamp.org →Production RAG with LangChain and Vector Databases
The follow-on course for when your prototype RAG needs to survive real users.
Open freecodecamp.org →Function calling (OpenAI API docs)
The primary source for letting the model trigger your app's actions, the step from chatbot to feature.
Open developers.openai.com →Introducing Structured Outputs in the API
Guaranteed-JSON responses ended a whole class of parsing bugs; read this before writing a parser.
Open openai.com →Builders Guide to the AI SDK
Free structured course covering prompting, extraction, streaming, and tool calls in real product code.
Open vercel.com →What We Learned from a Year of Building with LLMs (Part I)
Six practitioners condense a year of production LLM scar tissue into tactical advice.
Open oreilly.com →Patterns for LLM systems: evals, RAG, fine-tuning, caching, guardrails
The whole LLM-product stack in one thread; a map of every decision you will eventually face.
Open x.com →AI Engineering: Building Applications with Foundation Models
The most-read O'Reilly book of 2025; the reference text once you outgrow tutorials.
Open oreilly.com →Build Your First LLM Application: A Beginner's Tutorial
An India-based walkthrough from API key to working app, aimed at non-ML developers.
Open analyticsvidhya.com →Your First OpenAI API Project in Python, Step-By-Step
The gentlest possible on-ramp if your team writes Python but has never called a model API.
Open machinelearningmastery.com →From Idea to Product in 30 Min Using AI Agents | Claire Vo
A CPO livebuilds a real feature with AI tools, showing how fast the idea-to-ship loop has become.
Open creatoreconomy.so →Vercel AI SDK
The most popular TypeScript toolkit for streaming AI features, with 25+ providers behind one API.
Open github.com →Vercel AI Chatbot template
A production-ready open-source chatbot with auth and persistence; fork it instead of starting from zero.
Open github.com →More in AI in Your Product
- Should my startup add AI features, and which ones actually matter to users? 20 →
- How much does it cost to run AI features, and how do I keep costs sane? 19 →
- Which AI model should I build on (GPT, Claude, Gemini, open-source), and how do I avoid locking myself in? 17 →
- How do I test whether my AI feature actually works before launch (evals, in plain terms)? 18 →
- How do I stop my AI feature from making things up in front of customers? 17 →
- Prompt engineering, RAG, or fine-tuning: which one does my product actually need? 18 →
- How do I design AI features people trust (chat vs buttons, streaming, showing the work)? 18 →
- What is my moat if I'm building on someone else's model? 19 →
- Should I put an AI agent in my product, or is a simple workflow enough? 18 →
- Is it safe to send customer data to AI APIs, and what about privacy laws like India's DPDP Act? 18 →
- How should I price AI features: per seat, per use, or per outcome? 17 →
- My AI feature is live. How do I monitor it and make it better with real usage? 18 →
Also in Starting Up
The same ground, over in Build the product, our Starting Up track.
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