Optimization for the Agentic AI Era
A learning hub for learners and builders who want to measure, iterate, and improve AI systems — starting with DSPy, evaluations, and practical LLM workflows.
Built for developers, analysts, operators, and AI-curious professionals turning concepts into working systems.
A practical path into DSPy.
We are starting narrow on purpose. The first version of Optimizer University focuses on the DSPy concepts most learners need before the broader world of agentic AI optimization starts to make sense.
Curated, not cluttered.
Early on, this library stays small. We will add more videos, field notes, docs, and community links as they earn their place.
Better AI systems come from better learning loops.
DSPy gives us a sharp starting point, but the larger discipline is broader: define the goal, measure behavior, diagnose failures, and improve the system with evidence.
Define
Name the task, success criteria, inputs, outputs, and failure modes.
Measure
Build examples, metrics, and evaluations that reveal behavior.
Iterate
Test prompts, signatures, modules, retrieval, tools, and workflows.
Improve
Ship better behavior because the evidence says it is better.
Watch us learn and build in public.
Our channel will collect practical walkthroughs, DSPy explainers, optimizer experiments, and mental models for learners and builders working through the new AI stack.
Get updates as the curriculum takes shape.
Join the early list for new resources, videos, field notes, and community updates. For now, this form is a placeholder until email capture is connected.