Can eval setup be automatically scaffolded?

Yes, most of it can. You can auto-generate the boring parts: test case templates, prompt wrappers, JSON checks, basic metrics, and a simple report. The key is to keep it repeatable, not fancy. If your eval setup takes “a full day every time,” you’re not alone. This is one of the biggest hidden time drains in AI work. Why eval feels painful (and why it keeps getting skipped) 🔥 Eval is supposed to keep you safe.
But the setup feels like punishment: So people avoid eval until it’s too late. A simple “scaffolded eval” flow (the one that actually works) Here’s the boring stuff you can automate: The Eval Pack structure (scaffold in minutes) Copy-paste template: eval cases (JSONL) Each line is one test case: Copy-paste checklist: what to automate ✅ 1) Scaffolding checklist ✅ 2) JSON reliability checklist (huge time saver) ✅ 3) Metrics checklist (start small) ✅ 4) Report checklist (make it readable) Failure modes --> how to spot them --> how to fix them 1) My eval is slow so nobody runs it Spot it: people run it once a week, not per change
Fix: keep a smoke eval (10–20 cases) that runs fast, plus a longer nightly eval 2) The model returns broken JSON and ruins the pipeline Spot it: lots of parse error failures, no useful metrics
Fix: schema-first pipeline: validate, repair, validate, fail with raw output saved 3) Metrics look better but the product got worse Spot it: pass rate up, but user complaints increase
Fix: add a few real-world cases and track regression diffs, not just one number 4) We can’t tell if it improved or just changed Spot it: results are different every run
Fix: keep a baseline, compare diffs, and store the run artifact every time We’re building HuTouch to automate the repeatable layer (scaffolding, JSON checks, basic metrics, and reports), so engineers can focus on the judgment calls, not the plumbing. If you want to automate the boring parts of eval setup fast, try HuTouch: https://HuTouch.com How many eval cases do I need?
Start with 20–50 good ones. Add more only when you have repeatable failures. What’s the fastest metric to start with?
Schema pass rate + required fields pass rate + baseline diff. How do I eval agents, not just prompts?
Treat the agent like a function: same input --> get output --> validate --> score --> compare. Should I use LLM-as-a-judge?
Only after you have basic checks. Judges can help, but they can also hide problems. How do I stop eval from becoming a giant project?
Keep the first version small: fixed test set, fixed runner, basic report. Grow later. What should I store after each run?
Inputs, raw outputs, validated outputs, metrics, and a short report. That’s your replay button. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK:
Prompt / Agent Change | v
Run Eval Pack (same script every time) - load test cases - call model - validate JSON - compute metrics - compare to baseline | v
Report (what improved, what broke, what drifted) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
Prompt / Agent Change | v
Run Eval Pack (same script every time) - load test cases - call model - validate JSON - compute metrics - compare to baseline | v
Report (what improved, what broke, what drifted) CODE_BLOCK:
Prompt / Agent Change | v
Run Eval Pack (same script every time) - load test cases - call model - validate JSON - compute metrics - compare to baseline | v
Report (what improved, what broke, what drifted) CODE_BLOCK:
{"id":"case_001","input":"Summarize this support ticket...","expected_json_schema":"ticket_summary_v1","notes":"Must include priority + next_action"}
{"id":"case_002","input":"Extract tasks from this PR description...","expected_json_schema":"task_list_v1","notes":"Must include title + owner + due_date if present"} Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
{"id":"case_001","input":"Summarize this support ticket...","expected_json_schema":"ticket_summary_v1","notes":"Must include priority + next_action"}
{"id":"case_002","input":"Extract tasks from this PR description...","expected_json_schema":"task_list_v1","notes":"Must include title + owner + due_date if present"} CODE_BLOCK:
{"id":"case_001","input":"Summarize this support ticket...","expected_json_schema":"ticket_summary_v1","notes":"Must include priority + next_action"}
{"id":"case_002","input":"Extract tasks from this PR description...","expected_json_schema":"task_list_v1","notes":"Must include title + owner + due_date if present"} - you copy prompts into random files
- you track results in a messy sheet
- JSON outputs break and waste hours
- metrics change without explanation
- you can’t tell if the model improved… or just got lucky - Create an eval pack (folders + files)
- Generate a test set template (cases + expected outputs)
- Wrap the model call (same format every time)
- Validate outputs (especially JSON)
- Score results (simple metrics first)
- Compare to baseline (did it improve or just change?)
- Print a report (so anyone can read it) - eval_cases.jsonl (one test per line)
- schemas/ (your JSON schemas)
- runner.py (runs all cases)
- metrics.py (basic scoring)
- baseline.json (last known good results)
- report.md (auto-written summary)
This structure makes eval repeatable and easy to share with a teammate. - Create folder structure (Eval Pack)
- Create eval_cases.jsonl template
- Create baseline file stub
- Create a single command to run everything - Validate output is valid JSON
- Validate it matches a schema
- If invalid: attempt safe repair (then re-validate)
- If still invalid: mark as failure + store raw output - pass/fail rate (schema pass)
- exact match for small fields (when applicable)
- “contains required fields” (for structured outputs)
- regression diff vs baseline - total cases
- top failures (with IDs)
- what changed vs baseline (good + bad)
- links/paths to raw outputs for debugging