Course
AI Evals for Engineers and PMs
Intermediate
Use this when you want Shreya Shankar's material for evals and related AI skills.
AI educator
AI Evals for Engineers and PMs
Useful if you need to judge whether an AI feature is actually improving.
Start with: Review the course outcomes and pair it with a real feature you can evaluate.
Course
Intermediate
Use this when you want Shreya Shankar's material for evals and related AI skills.
Engineers, PMs, AI product teams should start here when they need evals, llm reliability, and product quality. The strongest fit is a learner who wants material in these formats: course, essays.
Review the course outcomes and pair it with a real feature you can evaluate. After that, open one related resource below and write down the exact workflow, concept, or implementation pattern you want to apply.
Useful if you need to judge whether an AI feature is actually improving. 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 evals, 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. |
|
AI Product Management Specialization
Duke University
|
Specialization | Beginner to intermediate | You want a structured product-management route for scoping, evaluating, and shipping AI products. |
|
Union Alpha revealed as Pareto 26.9
Unbiased
|
Multi-model routing launch explainer | Intermediate to advanced | Union Alpha was revealed as Unbiased's Pareto 26.9 on September 17, 2026: a hosted system that routes work across several models and returns one checked answer. |
|
The Anatomy of Harness Engineering
Google Developers Blog
|
Coding agent evaluation guide | Intermediate to advanced | You want Google's September 9, 2026 guide to evaluating coding agents with small behavioral checks, outcome-based assertions, and batch runs that catch regressions without treating a single benchmark score as the whole story. |
|
How Meta built safety into Muse
Meta AI
|
Agent security architecture guide | Intermediate to advanced | You want Meta's September 8, 2026 technical account of defense-in-depth for a long-running personal agent, including isolated runtime cells, credential surrogates, a separate permission authority, tainted-egress tracking, scoped approvals, browser controls, red teaming, and prompt-injection evals. |
|
Agentic models, measured on the injections that move money
Hugging Face Community
|
Open security benchmark | Intermediate to advanced | You want a reproducible September 5, 2026 benchmark for comparing how agentic models handle indirect prompt injection, with public data, a public harness, control runs, tool-call traces, and outcome metrics tied to unauthorized payment actions. |
|
How GitHub makes AI coding more cost efficient
GitHub
|
Coding agent evaluation guide | Intermediate to advanced | You want GitHub's September 2, 2026 evidence for measuring coding-agent efficiency across the whole task, including selective output compression, preserving useful context, benchmark regressions, and controlled production experiments. |
|
Project HydraFusion
GitHub
|
Model orchestration research preview | Intermediate to advanced | You want GitHub's September 4, 2026 technical explanation of runtime model orchestration for coding tasks, including plan decomposition, draft-critique-revise patterns, model cascading, evaluation design, and quality-versus-cost tradeoffs. |