Patterns in Practice

Agent orchestration is an emerging skill. Learn the mindset, technical abilities, and practices that make someone effective at directing AI agents.

How to architect AI agent workflows that actually work. Task decomposition, coordination patterns, and common pitfalls in agent workflow design.

When should humans intervene in AI agent workflows? Patterns for approval gates, oversight mechanisms, and maintaining control without killing productivity.

AI agents fail in weird ways. Learn debugging strategies, monitoring approaches, and observability patterns for understanding what your agents are actually doing.

Move beyond prompt tricks to engineering discipline. Patterns for maintainable prompts, version control, testing strategies, and scaling to production.

LLMs return text, but systems need structure. Master JSON mode, function calling, and validation patterns for reliable structured output extraction.

How do you know if your LLM is doing a good job? Evaluation metrics, benchmark selection, and practical approaches to measuring quality in production.

The most common AI architecture decision: RAG or fine-tuning? A practical framework for choosing the right approach based on your use case, data, and constraints.

Embeddings are the foundation of semantic search and RAG. Understand how they work, compare popular models, and learn how to choose the right one.

Move from RAG prototype to production. Architecture patterns for scale, hybrid search approaches, caching strategies, and hard-won lessons from real deployments.