A 0 to 1 Practical Guide to LLM Evals
Jampolhz · reddit · 2026-08-10
Noting a lack of accessible tutorials on LLM evaluation, the author created a practical, from-scratch video walkthrough.
Topics covered include:
- What an eval actually is and why "testing a few prompts and shipping" fails.
- Building test cases and datasets.
- Comparing code metrics vs. LLM-as-a-judge.
- Setting thresholds, analyzing failure reasons, and tracing.
- Fixing a failure and rerunning the same test cases.
The demo uses the open-source tool DeepEval, but the methodology applies to any stack.
More from Research
- Artificial Analysis Seeks Beta Testers for New AI Benchmarking Products — ArtificialAnlys · 2026-08-11
- New Research: Programmatic Tool Calling Beats Native JSON in Accuracy — dair_ai · 2026-08-11
- Interview with Lean Creator: LLMs Combined with Formal Verification to Revolutionize Math and Software — dejavucoder · 2026-08-11
- Brainwave-Based System Offers Objective Way to Assess Pain Intensity — WmHaseltine · 2026-08-11
- AI4Science Paper: Exploring Latent Space of Molecular Generative Models — AllThingsApx · 2026-08-11
- UHAS: A Unified Action Space for Cross-Embodiment Robot Manipulation — DJiafei · 2026-08-11