Evaluation & Guardrails

Why do chatbots forget context? The difference between vector 'memory' and true 'state.' How to use state machines (LangGraph) to maintain variable integrity across a 50-step process.

If your tool definition is vague, your agent will fail. Best practices for Pydantic validation, error handling, and designing 'unbreakable' tools that recover gracefully from bad LLM calls.

Never let an agent push code to production without a review. How to build a 'Critic' agent that reviews, lints, and rejects the work of the 'Builder' agent before a human ever sees it.

If you don't test it, you can't deploy it. But how do you unit test a probability engine? Strategies for 'LLM-as-a-Judge,' deterministic mocking, and continuous evaluation pipelines.

Context windows are finite but conversations aren't. Learn production strategies for context management, summarization, and smart token utilization.

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.

RLHF made ChatGPT useful. Understanding how reinforcement learning shapes AI behavior helps you understand what AI can—and can't—become in your organization.

Models don't fail all at once—they drift. Learn to detect data drift, concept drift, and model drift before small degradations become major production failures.