DietrichGebertandClaude Opus 4.8
caf138df56
* fix(benchmarks): correctness gate scores unfenced code; fix debounce task The `correct` gate under-reported correctness for terse models, the likely source of "Ponytail degrades models" reports (issue #65): - extractBlocks() only matched fenced code blocks, so bare/unfenced code scored an automatic fail even when correct. Now falls back to the whole response as one block (and tolerates CRLF). Debounce detection also accepts unfenced arrow functions. - The debounce task asked to "add debounce to a search input" but the check expected a reusable debounce(fn, delay) util, failing correct inline answers. Task reworded to the deliverable the check verifies. Adds correctness.test.js (regression guard) and a GPT-mini repro config plus results writeup: on a clean n=20 run, the reported gpt-4.1-mini drop (10/15) does not reproduce (100/100). The LOC win (~halved) holds. README repro fixed: promptfoo needs --env-file ../.env (reads cwd, not root). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(benchmarks): add robustness audit — ponytail vs baseline on edge cases Answers the real question behind #65: does ponytail's push for the shortest solution make weak models produce wrong code on edge cases? robustness-audit.js: 16 self-verifying tasks (12 algorithmic edge-case traps + 4 validators). Each check ships a known-good and known-lazy-wrong reference that must pass/fail before any model output is scored (--selftest, 16/16). Findings (gpt-4.1-mini + gpt-5.4-mini, baseline vs ponytail): parity on every edge-case trap on both models. The one measured soft spot is gpt-5.4-mini email (~4-5%, reaches for parseaddr). A sharpened SKILL.md validation rule had no reliable effect in an n=100 A/B (96% vs 95%), so it was not shipped — the tendency is model-level, not skill-level. Full writeup in results/. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(benchmarks): email slip is provider-specific — 100% on Claude High-n cross-provider follow-up to the robustness audit. The one ponytail soft spot (email validation via parseaddr) splits by provider, not model size: - Claude (haiku/sonnet/opus): 100% under ponytail, n=40 each — and ponytail beats baseline (unconstrained Sonnet over-engineers into an always-truthy dict, 0/40; ponytail writes a clean validator). - OpenAI (gpt-4.1-mini..gpt-5.5): slips at every size under ponytail (~79-98%), baseline ~100%. The parseaddr reflex lives in OpenAI training. Not fixable by skill text: 8 distinct SKILL.md edits (incl. an n=100 A/B, 96% vs 95%) all scored <= current, several worse, all bloated LOC. Nothing shipped. SKILL.md unchanged. Conclusion: on ponytail's target platform (Claude) email is 100%; the GPT slip is a documented cross-provider transfer quirk. Adds model-email.js / claude-email.js to reproduce the tables. Writeup updated. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs(benchmarks): correct misleading Sonnet baseline 0 percent The Sonnet baseline 0/40 on email is a return-type artifact, not a logic failure: unconstrained Sonnet returns a dict {is_valid, message} instead of a bool, so the bool-contract gate scores every case as accepted. Read dict-aware via is_valid, its logic is ~75% correct (9/12). Reframed honestly so we are not presenting 0 vs 100 as a clean win; ponytail still wins (clean 100% bool) but the point is over-engineered return type, not total failure. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
42 lines
1.8 KiB
YAML
42 lines
1.8 KiB
YAML
# Ponytail benchmark: code size + cost across three arms, same model, same tasks.
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#
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# Run: npx promptfoo@latest eval -c benchmarks/promptfooconfig.yaml
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# View: npx promptfoo@latest view
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# Share: npx promptfoo@latest share (publishes a hosted report URL)
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#
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# Needs ANTHROPIC_API_KEY in the environment or a .env file (see benchmarks/README.md).
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# Caveman arm uses JuliusBrussee/caveman SKILL.md (MIT), vendored at arms/caveman-SKILL.md.
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description: "Ponytail vs caveman vs no-skill: same model, same tasks. Measures code LOC (deterministic) and tokens/cost (API telemetry)."
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providers:
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- id: anthropic:messages:claude-haiku-4-5-20251001
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config: { max_tokens: 8192, temperature: 1 }
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- id: anthropic:messages:claude-sonnet-4-6
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config: { max_tokens: 8192, temperature: 1 }
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- id: anthropic:messages:claude-opus-4-8
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config: { max_tokens: 8192, temperature: 1 }
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prompts:
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- id: file://arms/baseline.js
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label: baseline (no skill)
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- id: file://arms/caveman.js
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label: caveman
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- id: file://arms/ponytail.js
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label: ponytail
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defaultTest:
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assert:
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- type: javascript
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value: file://loc.js
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metric: code_loc
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- type: javascript
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value: file://correctness.js
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metric: correct
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tests:
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- vars: { task: "Write me a Python function that validates email addresses." }
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- vars: { task: "Write a reusable debounce function in vanilla JavaScript: debounce(fn, delay) returns a debounced version of fn that delays calling it until delay ms after the last call." }
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- vars: { task: "Write Python code that reads sales.csv and sums the 'amount' column." }
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- vars: { task: "Build me a countdown timer component in React that counts down from a given number of seconds." }
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- vars: { task: "Add rate limiting to my FastAPI endpoint so users can't spam it." }
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