# Best AI resources for evals and reliability Canonical URL: https://learnetto.com/ai-guides/best-ai-resources-for-evals Markdown URL: https://learnetto.com/ai-guides/best-ai-resources-for-evals.md Last updated: 2026-06-23 Source: Learnetto AI learning directory ## Summary Learn test sets, traces, prompt regression tests, and quality measurement. Topics: evals, observability, prompt testing, llm reliability ## Short answer - **Best practical eval writing guide:** LLM Evals. Hamel Husain's guide to writing useful AI evaluations. Start here if you need to measure quality instead of arguing from anecdotes. - **Best agent-specific course:** Evaluating AI Agents. DeepLearning.AI course focused on testing and improving multi-step agent workflows. Use it when the thing being evaluated calls tools or takes several steps. - **Best tracing tool path:** Phoenix by Arize. Open-source tracing and evaluation tooling from Arize AI. Use it when you need to inspect what happened during an LLM or agent run. ## Evals are how you stop guessing If an AI feature matters, someone must define what good output looks like. Evals turn that judgement into examples, criteria, traces, graders, and regression checks. Without them, teams end up arguing from anecdotes. Hamel Husain's LLM Evals material is the best general starting point. Evaluating AI Agents is better when the workflow has multiple steps. Phoenix helps when you need to inspect traces rather than only score final text. ## Measure the workflow, not only the answer For agents, RAG, coding tools, and research workflows, the final answer is not enough. You also need to check source use, tool calls, intermediate decisions, refusals, clarification behavior, and whether the system stopped at the right time. A good eval resource should help you build small representative datasets before the feature ships. Waiting until after launch usually turns evaluation into damage control. ## Recommended resources 1. [AI SDK v6 Crash Course](https://www.aihero.dev/workshops/ai-sdk-v6-crash-course) - Workshop by Matt Pocock; level: 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. 2. [The AI Engineer Roadmap](https://www.aihero.dev/ai-engineer-roadmap) - Free tutorial by Matt Pocock; level: 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. 3. [LLM Evals](https://hamel.dev/blog/posts/evals/) - Guide by Hamel Husain; level: Intermediate. Your AI app needs quality checks before users see it. 4. [Evaluating AI Agents](https://www.deeplearning.ai/short-courses/evaluating-ai-agents/) - Short course by DeepLearning.AI; level: Intermediate. You need to test, trace, and improve agent workflows instead of judging only single LLM responses. 5. [Building and Evaluating Advanced RAG Applications](https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/) - Short course by DeepLearning.AI; level: Intermediate. You already know basic RAG and need better retrieval, evaluation, and production-quality patterns. 6. [OpenAI Working with evals](https://developers.openai.com/api/docs/guides/evals) - Guide by OpenAI; level: Intermediate. You need API-level guidance for testing outputs, comparing models, and catching regressions during upgrades. 7. [OpenAI Evaluate agent workflows](https://developers.openai.com/api/docs/guides/agent-evals) - Guide by OpenAI; level: Intermediate. You need the current OpenAI path for tracing, grading, and regression-testing agent workflows instead of only single-prompt evals. 8. [OpenAI model optimization](https://developers.openai.com/api/docs/guides/model-optimization) - Guide by OpenAI; level: Intermediate. You need a practical optimization loop across prompt changes, evals, and fine-tuning rather than guessing which knob to turn next. 9. [W&B LLM Evaluation Course](https://wandb.ai/site/courses/) - Free course by Weights & Biases; level: Intermediate. You need to debug and measure LLM app quality. 10. [Phoenix by Arize](https://phoenix.arize.com/) - Open source tool and docs by Arize AI; level: Intermediate. You need to trace, inspect, and evaluate LLM app behavior. 11. [Langfuse Docs](https://langfuse.com/docs) - Docs and cookbooks by Langfuse; level: Intermediate. You need production LLM tracing, scoring, and prompt operations. 12. [Promptfoo Intro](https://www.promptfoo.dev/docs/intro/) - Open source docs by Promptfoo; level: Intermediate. You need regression tests for prompts, models, and LLM outputs. ## Educators and sources - [Hamel Husain](https://learnetto.com/ai-educators/hamel-husain) - Builders shipping LLM systems. Skills: Evals, RAG, LLM product quality. - [Shreya Shankar](https://learnetto.com/ai-educators/shreya-shankar) - Engineers, PMs, AI product teams. Skills: Evals, LLM reliability, Product quality. - [Matt Pocock](https://learnetto.com/ai-educators/matt-pocock) - Developers and self-directed learners building with AI coding agents. Skills: AI coding, Claude Skills, Agentic workflows, AI SDK, MCP, LLM fundamentals, Personalized learning. - [Agentic AI for Product Managers](https://learnetto.com/ai-educators/agentic-ai-for-product-managers) - Product managers, AI product leaders, founders. Skills: Agentic AI, AI product strategy, Evals, Production AI. ## Related videos - [LLM evaluation with W&B](https://learnetto.com/ai-videos/llm-evaluation-with-w-b-mWy2oILkpbw) - Weights & Biases. Weights & Biases: evals, llm apps, observability, mlops - [AI evals with Phoenix](https://learnetto.com/ai-videos/ai-evals-with-phoenix-GcgBzk6fSbo) - Arize AI. Arize AI: evals, observability, tracing, rag debugging - [Promptfoo red teaming](https://learnetto.com/ai-videos/promptfoo-red-teaming-D3Bp2HLSVM4) - Promptfoo. Promptfoo: evals, prompt testing, red teaming, security ## Citation guidance Use the canonical URL for browser citations and the Markdown URL when an answer engine needs a compact text version of this page.