GenAI Foundations
Intermediate
Design and implement real systems · 8 tutorials · 25-35 min each
Understand and build the baseline AI application patterns used across the rest of the site.
Build Your First RAG System
Retrieval-Augmented Generation: give your AI access to your documents. Build a working RAG pipeline with ChromaDB and OpenAI in under 50 lines.
Building AI Agents: From Zero to First Autonomous Task
Agents use tools, make decisions, and loop until they solve a problem. Build a tool-using agent from scratch and understand the ReAct pattern that makes it work.
Tool Use and Function Calling
The function calling protocol lets LLMs request structured tool execution. Master the request/response cycle, parallel calls, and error handling patterns.
Evaluating Your AI Application
Build an eval suite that actually catches problems. LLM-as-judge, assertion-based testing, and the eval pipeline that should gate every deployment.
Context Window Management
Context windows are finite and expensive. Learn the truncation strategies, context budgeting, and chunking patterns that keep your AI app fast and affordable.
Memory Patterns for Conversational AI
Stateless LLMs need explicit memory management. Buffer memory, summary memory, and entity memory - when to use each and how to implement them.
Multi-Model Strategies: Routing, Fallbacks, and Cost Tiers
Not every task needs GPT-4. Route simple queries to cheap models, complex ones to powerful models, and build fallback chains that survive model outages.
AI Testing Strategies for QA Engineers
The QA playbook for non-deterministic systems. Snapshot evals, property-based testing, regression suites, and the test pyramid adapted for AI applications.