MLOps & Observability

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.

Fine-tuning is powerful but often misused. Learn when to fine-tune, how to do it right (cloud and local), and why prompt engineering or RAG might be better choices.

The future isn't bigger models—it's smarter small ones. Learn how to distill large models into efficient, task-specific versions for production deployment.

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.