Maven cohort course
Hamza Farooq on Maven
Beginner to intermediate
Use this when you want Agentic AI for Product Managers's material for agentic ai and related AI skills.
AI educator
Hamza Farooq on Maven
Useful for PMs who need to design, evaluate, and ship reliable AI systems beyond impressive demos.
Start with: Use the course to evaluate one AI product opportunity and define what reliability would mean before implementation.
Maven cohort course
Beginner to intermediate
Use this when you want Agentic AI for Product Managers's material for agentic ai and related AI skills.
Product managers, AI product leaders, founders should start here when they need agentic ai, ai product strategy, evals, and production ai. The strongest fit is a learner who wants material in these formats: maven cohort course, case studies.
Use the course to evaluate one AI product opportunity and define what reliability would mean before implementation. After that, open one related resource below and write down the exact workflow, concept, or implementation pattern you want to apply.
Useful for PMs who need to design, evaluate, and ship reliable AI systems beyond impressive demos. Use this profile when you are comparing educators by topic, level, format, and practical usefulness rather than browsing random AI content.
Compare the skill coverage, the starting recommendation, the educator's own resources, and any videos when available. If you need agentic ai, search the directory for that skill and shortlist three profiles before committing to a course, book, or playlist.
| Resource | Kind | Level | Use when |
|---|---|---|---|
|
AI SDK v6 Crash Course
Matt Pocock
|
Workshop | Intermediate | You want a structured AI SDK v6 course that covers model choice, text and object generation, UI streams, agents, persistence, context engineering, evals, and advanced app patterns. |
|
The AI Engineer Roadmap
Matt Pocock
|
Free tutorial | Beginner to intermediate | You want a guided path through core AI concepts, model selection, the AI engineering mindset, evals, and techniques for improving LLM-powered apps. |
|
LLM Evals
Hamel Husain
|
Guide | Intermediate | Your AI app needs quality checks before users see it. |
|
Evaluating AI Agents
DeepLearning.AI
|
Short course | Intermediate | You need to test, trace, and improve agent workflows instead of judging only single LLM responses. |
|
Building and Evaluating Advanced RAG Applications
DeepLearning.AI
|
Short course | Intermediate | You already know basic RAG and need better retrieval, evaluation, and production-quality patterns. |
|
Promptfoo
OpenAI / Promptfoo
|
Open-source eval and red-team framework | Intermediate to advanced | Declarative regression tests and side-by-side comparisons of prompts, models, RAG systems, and agent configurations, especially when red teaming is also required. |
|
DeepEval
Confident AI
|
Pytest-style LLM evaluation framework | Intermediate to advanced | Engineering teams that want LLM and agent evaluations to behave like software unit tests, with thresholds, assertions, and CI-friendly failures. |
|
Ragas
Vibrant Labs
|
RAG and AI-application evaluation library | Intermediate to advanced | Evaluating retrieval quality, grounded generation, agent tool use, and production-aligned test data for RAG applications. |
|
Inspect AI
UK AI Security Institute
|
Model and agent evaluation harness | Intermediate to advanced | Rigorous, reproducible model and agent capability or safety evaluations involving tools, multi-turn interaction, coding, or sandboxed environments. |
|
Arize Phoenix
Arize AI
|
AI observability and evaluation platform | Intermediate to advanced | Teams that want evaluation connected to OpenTelemetry traces, datasets, experiments, prompt iterations, and production troubleshooting. |
|
OpenEvals
LangChain
|
Reusable evaluator library | Intermediate to advanced | Python or TypeScript developers who want composable evaluator functions without adopting a full evaluation platform. |
|
Giskard
Giskard AI
|
Agent testing and red-team framework | Intermediate to advanced | Behavioral tests and adversarial scans of multi-turn agents, chatbots, and RAG systems from a pytest-compatible workflow. |