Cite as: Real Problem AI problem “Why does every team rebuild the same AI eval harness from scratch?”. Opportunity score 7.8 out of 10 (severity 7, AI feasibility 9, market signal 8, competition gap 7). Category AI / Agents. Trend LLMOps. Source signal: Hamel Husain's "evals" blog series, Eugene Yan's posts (2026), Latent Space Discord, applied-LLM startup repos.. Canonical URL: https://www.realproblem.ai/archive/why-does-every-team-rebuild-the-same-ai-eval-harness-from-scratch.
Why does every team rebuild the same AI eval harness from scratch?
Every AI team writes its own evals: golden sets, judges, scoring, regressions. Mostly the same code, rewritten badly, by every team.
Who has it: AI/ML engineers at startups (5-50 people) shipping LLM features to customers.
Evidence
Engineers describe writing the same harness at startup after startup: load a test set, run the prompt, judge with a model, write results to a sheet.
Our summary of a complaint that recurs in public posts, not a quote. Nobody submitted it to Real Problem AI.
Seen in: Hamel Husain's "evals" blog series, Eugene Yan's posts (2026), Latent Space Discord, applied-LLM startup repos.Scoring breakdown
Existing players
- Promptfoo · Open-source, dev-only
- Braintrust · Closer, opinionated SaaS
- LangSmith evals · Coupled to LangChain
What they are missing
An eval-harness primitive that ships with every LLM SDK: opinionated defaults, judge picker, automatic regression detection on PRs, and exportable golden sets. Not a SaaS, a library + dashboard combo.
Stack hint
#AI23 · Canonical URL: https://www.realproblem.ai/archive/why-does-every-team-rebuild-the-same-ai-eval-harness-from-scratch