LLM Core — From Tokens to Apps

What you'll build

• Tokenomics: costs, latency, and context physics
• Prompt Engineering from Zero-shot to CoT
• Structured Outputs & JSON Mode with Zod
• FastAPI for robust AI service backends

Lessons

1. Tokens, Latency, and the Physics of LLMs

   LLMs don't see words, they see tokens. Learn how tokenization affects cost, latency, and model behavior — the fundamental physics of every AI app.

2. Prompt Engineering Mastery

   Prompting is the new programming. Learn how to move from basic questions to complex techniques like Chain-of-Thought and Few-shot prompting.

3. Structured Outputs & JSON Mode

   AI systems are only useful if their outputs are predictable. Learn how to force LLMs to return valid JSON that your code can actually parse.

4. FastAPI for AI Backends

   Python is the language of AI, and FastAPI is the framework of choice for serving it. Build robust, asynchronous APIs to wrap your models.

5. Context Engineering & Hallucination Reduction

   It's not just what you ask, but what you provide. Learn how to structure context to maximize accuracy and minimize model hallucinations.

6. State, Memory & Conversation Flow

   LLMs are stateless. To build a chat app, you must manage state. Learn the patterns for short-term and long-term conversation memory.

7. Streaming & UX Patterns

   Users hate waiting. Learn how to stream tokens in real-time and build AI interfaces that feel 'alive' using Server-Sent Events (SSE).