LLM Foundations
Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration. Covers transformer anatomy, inference optimization, and alternative architectures. Prepares Python developers for production agent development.
Learning Path
5 phases · 20 chaptersIntermediate Python
Advanced Python patterns for GenAI development
Tools & Topics
Generators, async/await, type hints, Pydantic, data pipelines, HTTP clients
Goals
- •Build data processing pipelines with generators
- •Write async Python code for concurrent operations
- •Use type hints and Pydantic for validation
- •Work with data pipelines and transformations
Chapters
LLM Fundamentals
Core LLM concepts and API integration
Tools & Topics
First API call, tokenizer internals, context windows, inference phases
Goals
- •Make your first LLM API call
- •Understand tokenization and BPE algorithm
- •Manage context windows and KV-cache
- •Understand LLM architectures (Dense, MoE)
Chapters
LLM Architecture Deep Dive
Understanding model internals and alternatives
Tools & Topics
Transformer layers, attention patterns, FFN variants, SSMs, hybrid models
Goals
- •Understand transformer layer anatomy
- •Analyze attention patterns and mechanisms
- •Compare FFN variants and activation functions
- •Explore SSMs and hybrid architectures
Chapters
AI-Ready Python
Production-ready LLM integration patterns
Tools & Topics
Sampling, prompt engineering, multi-provider
Goals
- •Control LLM outputs with sampling parameters
- •Apply prompt engineering techniques
- •Build multi-provider LLM integrations
- •Use Jinja2 for prompt templates
Chapters
Production Readiness
Essential patterns for production LLM applications
Tools & Topics
Function calling, embeddings, RAG, cost awareness, retry patterns
Goals
- •Implement function calling with LLM APIs
- •Build semantic search with embeddings
- •Create RAG pipelines for knowledge retrieval
- •Track and optimize token costs