LLM Application Engineering

Every lesson: deep notes, curated lectures, an animated explainer, a hands-on lab, practice questions and a timed exam.

1. How LLMs are served: tokens, context windows, sampling, cost and latency maths
2. One client for many models: free-tier APIs and local models
3. Prompts that hold up: instructions, examples, delimiters and prompt versioning
4. Structured outputs: JSON schemas, Pydantic validation and retries
5. Function calling and tool use
6. Context engineering: deciding what goes into the window
7. Multimodal inputs: images, PDFs and speech
8. Streaming, caching, rate limits and fallbacks
9. Serving an AI feature with FastAPI
10. Front ends for AI apps: Streamlit, then a first look at TypeScript and Next.js
11. Privacy and responsible AI, including India's DPDP Act 2023
12. Module project

Tools introduced: FastAPI, Pydantic