Agentsop Domain Eval Set

domain-eval-set — Your Held-Out Domain Benchmark

> "Compiled program beats baseline on a *held-out* test set (not the val set used in optimization)."

> — DSPy SOP exit criterion [dspy.ai/learn/optimization/overview/]

> "Build the eval loop before optimizing anything. Every subsequent change must be gated on these numbers."

> — LlamaIndex SOP Stage 2

This is an ENHANCE overlay skill. It produces one artifact — a versioned,

sealed, human-labeled set of 50–200 examples drawn from *your* domain — that

other skills consume: [[agentsop-regression-gate]] enforces it on every PR,

[[agentsop-metric-design]] defines the scoring function applied to each example, and

[[lm-evaluation-harness]] runs the *complementary* public-capability axis. The

core claim: **public benchmarks tell you the model is smart in general; only a

held-out domain set tells you it works on your task.** The latter is the one that

predicts production.

1. 何时激活 (When to Activate)

Activate when any of these is true:

• "Does THIS system work on OUR data?" — someone is about to ship or trust an

LLM/RAG/agent system and the only evidence is vibes, a demo, or a public

benchmark number. You need a quantitative answer on the real distribution.

• A public-benchmark number is being used as a deployment gate. Someone cites

"92% on MMLU" or "passes HumanEval" to justify go-live. That measures general

capability, not your task fit (AP-1). Force a domain set into the decision.

• A model / prompt / retriever / chunking change needs a regression gate and

no domain test set exists yet to gate against. You must build the set before

[[agentsop-regression-gate]] can do its job.

• Switching models (GPT-4o → a cheaper or local model). The public-bench gap

may be small while the domain gap is large, or vice versa. Only your held-out

set tells you which.

• Production complaints don't match your eval scores. Either the set is stale

(refresh, OP-DE06) or it never reflected the domain (rebuild from real traffic).

Do NOT activate for:

• Pure capability comparison / academic reporting. "Which model is best at

MMLU/GSM8K?" → that is [[lm-evaluation-harness]], not this skill.

• One-off throwaway prototypes where no decision rides on quality and nothing

ships. Don't build a benchmark for a script you'll delete tomorrow.

• Tasks with an objective oracle already (compiler passes, exact DB match,

schema validity gives ≥95% of signal) — the "eval set" is just running the

oracle; you don't need curated held-out examples. Don't gold-plate.

2. 核心心智模型 (Core Mental Model)

"Public benchmarks measure general capability. A 50–200 example held-out domain set measures YOUR task. Only the latter predicts production."

Two orthogonal axes, constantly confused:

| Axis | What it measures | Tool | Predicts production? |

|---|---|---|---|

| General capability | Reasoning, knowledge, coding *in general*, on shared public tasks | [[lm-evaluation-harness]] (MMLU, HumanEval, GSM8K, TruthfulQA) | No — a proxy at best |

| Domain task fit | Whether the system answers *your* users on *your* data | this skill (held-out domain set) | Yes — this is the signal |

A model can score 90% on MMLU and 40% on your insurance-claims triage. A model

can score *below* SOTA on HumanEval and be perfect at your internal codebase's

patterns. The public number and the domain number are nearly uncorrelated once

you're past a basic capability floor. **The public bench is a sanity check; the

domain set is the decision.**

Three corollaries (each maps to an SOP stage):

• Real beats synthetic. The set is sampled from *real* domain traffic

(tickets, queries, logs, transactions), stratified, with edge cases pulled

deliberately. Auto-generated QA pairs (LlamaIndex DatasetGenerator) are a

fine *bootstrap*, but a model can ace generated questions and still fail real

user phrasing. Generated sets do not replace a real held-out set (§7).

• Held out means SEALED. The held-out split is never shown to the optimizer,

never pasted into a prompt as a few-shot demo, never used to pick chunk size

or reranker, never in the fine-tune data. The moment it leaks, the number is

inflated and meaningless (AP-2, OP-DE07). Per DSPy: the test set must be

*distinct from the val set used in optimization* [dspy.ai/learn/optimization/overview/].

• Small but significant. 50–200 examples. Below ~30 you are "memorizing, not

training" [dspy.ai/learn/optimization/overview/] and differences are noise. The

set is small enough to label by hand and large enough to detect ~5–10pp

regressions and to slice by segment.

3. SOP (Standard Operating Procedure)

0. Confirm activation (§1) — is the question "does this work on OUR data"?
1. COLLECT  — sample real domain examples; stratify; pull edge cases       (OP-DE01)
2. LABEL    — gold answer / reference / pass-fail; 2 annotators on subset  (OP-DE02)
3. HOLD OUT — split train/dev/test; SEAL the test split                    (OP-DE03)
4. SIZE     — land at 50-200; per-segment counts                           (OP-DE04)
5. VERSION  — hash + date + rubric; freeze as an artifact                  (OP-DE05)
6. LEAK-AUDIT — diff held-out vs demos / train / fine-tune data            (OP-DE07)
7. PAIR     — report alongside public bench; gate on the domain set        (OP-DE08)
   (later) REFRESH on domain shift                                         (OP-DE06)

Stage 1 — Collect from real traffic

Pull from where the real distribution lives: support tickets, search/query logs,

user transcripts, transaction records, bug reports. Stratify so the set

covers the production mix — by query type (lookup / summary / compare), by

segment (tenant, language, product area), by difficulty. Then **deliberately

over-sample edge cases and known failures** — the head of the distribution is

easy; the tail is where systems break.

Target a raw pool ≥ 2× the final size (you'll drop ambiguous items in labeling).

Record provenance and timestamp per example (needed later for drift refresh).

Exit: a candidate pool ≥ 2× target, with provenance, spanning the real mix.

Stage 2 — Label and curate

Attach ground truth per example: a gold answer, an *acceptable reference*

response (not "the unique correct" one for open-ended tasks — see

[[agentsop-metric-design]]), or a pass/fail label. For RAG, **also label the gold

passage** so RetrieverEvaluator(["mrr","hit_rate"]) can run [LlamaIndex OP-10].

Have two annotators label a subset, measure agreement, resolve disagreements,

and drop genuinely ambiguous items — an example two experts can't agree on

will only add noise. Record the rubric. (This is the data-side analogue of DSPy's

"human-validate the metric on ≥20 spot-checks" discipline [DSPy Case C].)

Exit: labeled set with inter-annotator agreement noted, rubric recorded,

ambiguous items logged as rejected.

Stage 3 — Hold-out discipline (the load-bearing stage)

Split into train / dev / test. The test (held-out) split is sealed:

• NEVER shown to an optimizer (DSPy trainset, MIPRO/GEPA).
• NEVER pasted into a prompt as a few-shot demo.
• NEVER used to pick chunk size / reranker / hybrid alpha / model.
• NEVER in fine-tune data.

Store it in a separate file/location with an access note. Per DSPy, the

exit-gate test set must be "distinct from the val set used in optimization"

[dspy.ai/learn/optimization/overview/]. The dev split is what you tune against;

the test split is the one number you trust at decision time.

Exit: sealed held-out test split + train/dev splits; access policy written.

Stage 4 — Size for 50–200

• 50 — minimum for a coarse production go/no-go signal.
• 100–200 — stable enough to detect ~5–10pp regressions and to slice per

segment (each slice needs its own ≥~30 to be meaningful).

• <30 — do not bother gating on it; the variance swamps the signal

[dspy.ai/learn/optimization/overview/].

Size up (toward 200, or split into per-segment sets each ~50) when you need

per-segment confidence. LlamaIndex's DatasetGenerator default of num=50 sits

at the low end of this band — fine to bootstrap, then curate.

Stage 5 — Version it

Freeze the set as a versioned artifact — eval_v1.jsonl plus a manifest with

a content hash, creation date, and the labeling rubric. Score every

model / prompt / retriever change against the *same* version; keep a results

table keyed by (eval_version, system_version); bump only on a deliberate

refresh, never silently. DSPy ships program.json as a versioned artifact

[dspy.ai/tutorials/saving/]; LlamaIndex versions indices as deployment artifacts

(SOP Stage 5) — the eval set deserves the same rigor.

Stage 6 — Leak audit

Before any release, and whenever few-shot demos or fine-tune data are assembled,

diff the held-out set against (a) prompt few-shot demos, (b) fine-tune /

training data, (c) the optimizer trainset. Any overlap = contamination → the

held-out number is inflated and worthless (AP-2). Remove the overlap or rebuild

the split — the same provenance discipline as [[agentsop-metric-design]]'s calibration

receipt (OP-M10).

Stage 7 — Pair with the public bench, gate on the domain set

Run [[lm-evaluation-harness]] for the capability floor (sanity check: is the

model fundamentally competent?). Run the domain held-out set for the decision.

Report both side by side. If they disagree, the domain set wins the go/no-go.

Hand the sealed set to [[agentsop-regression-gate]] to enforce on every subsequent PR.

Refresh — when the domain shifts

Domains drift: new product line, new user segment, seasonal change. When held-out

scores stop tracking production complaints, refresh (OP-DE06): add fresh real

examples from recent traffic, retire stale ones, re-label edge cases production

surfaced, bump the version, keep the old version for back-comparison. Cadence:

quarterly *or* on any major domain change, whichever comes first. (This mirrors

LlamaIndex's live-corpus reconciliation, A10.)

4. 操作模型 (Operations)

Each operation: Trigger → Action → Output [Evidence]. Full Trigger/Action/

Output/Evidence form in intermediate/operation_candidates.json.

• OP-DE01 SourceFromRealTraffic — No curated set, traffic available → sample

real inputs (logs/tickets/queries/transactions), stratify by type/segment/

difficulty, over-sample edge cases → raw pool ≥2× target with provenance.

[DSPy dev-set discipline; LlamaIndex OP-10 eval-from-corpus]

• OP-DE02 LabelAndCurate — Raw pool collected → attach gold/reference/pass-fail

per item; two annotators on a subset, resolve disagreement, drop ambiguous,

record rubric; for RAG label the gold passage → curated labeled set with

agreement noted. [DSPy Case C ≥20 spot-checks; LlamaIndex RetrieverEvaluator]

• OP-DE03 HoldOutDiscipline — Set about to be used → split train/dev/test; seal

the test split (never to optimizer, never as few-shot demo, never to pick

chunking/reranker/model, never in fine-tune data) → sealed test + train/dev.

[DSPy "held-out distinct from val"; Case A step 4]

• OP-DE04 SizeFor50to200 — Deciding size → target 50–200 (50 = coarse signal;

100–200 = detect ~5–10pp regressions + per-segment slices; <30 = noise) → sized

set with per-segment counts. [DSPy "30 min, 200+ for MIPROv2"; LlamaIndex num=50]

• OP-DE05 VersionTheSet — Set finalized → freeze as eval_v1.jsonl + manifest

(hash, date, rubric); score every change vs the same version; results keyed by

(eval_version, system_version); bump only on deliberate refresh → versioned

artifact. [DSPy program.json versioning; LlamaIndex versioned indices]

• OP-DE06 RefreshOnDomainShift — Domain drifts; scores stop tracking complaints

→ add fresh recent-traffic examples, retire stale, re-label edge cases, bump

version, keep old for comparison (quarterly or on major change) → new version +

drift log. [LlamaIndex live-corpus reconciliation A10]

• OP-DE07 LeakAudit — Before release / when demos or fine-tune data assembled →

diff held-out vs few-shot demos, fine-tune data, optimizer trainset; any overlap

= contamination → remove or rebuild → leak-audit report (0 overlap). [DSPy

held-out-distinct rule; metric-design provenance OP-M10]

• OP-DE08 PairWithPublicBench — Public-bench number used to justify deployment →

treat public bench as capability floor/sanity check, require the domain held-out

set as the decision gate; report both, on disagreement the domain set wins →

two-axis report gated on domain. [[[lm-evaluation-harness]] covers public, not

your domain]

5. 困境决策案例 (Dilemma Cases)

Dilemma 1 — "We don't have enough labeled domain data to build a held-out set"

困境: A team wants to ship a contract-review assistant. They have thousands of

raw contracts but only ~25 examples a lawyer has labeled with gold answers.

25 < the 50 floor and well below the 30 "memorizing, not training" line

[dspy.ai/learn/optimization/overview/]. They're tempted to (a) skip the held-out

set and ship on MMLU/legal-bench numbers, or (b) auto-generate 200 QA pairs with

LlamaIndex DatasetGenerator and call that the held-out set.

约束: Lawyer labeling time is the bottleneck (~$$/hour, scarce). Public legal

benchmarks exist but don't reflect this firm's contract templates. Auto-generated

questions risk testing "what the corpus says" rather than "what real reviewers

ask".

决策步骤:

• Refuse to gate on the public bench alone (AP-1). A legal-bench number is a

capability floor, not proof the assistant handles *these* contracts.

• Use auto-generation to bootstrap the DEV set, never the held-out test set.

DatasetGenerator (LlamaIndex Stage 2) gives a cheap dev set for iteration —

but it is synthetic, so it cannot be the trusted held-out number (§7 caveat).

• Spend the scarce labeling budget on the held-out set, not the dev set. Have

the lawyer label the *50 hardest real examples* (stratified, edge-case-heavy,

OP-DE01/02) rather than 200 easy generated ones. 50 real-labeled > 200

synthetic for the decision gate.

• Two-annotator a subset (OP-DE02) so you trust the gold labels; drop the

ambiguous ones rather than padding the count.

• Seal those 50 (OP-DE03), version them (OP-DE05). Iterate against the

synthetic dev set; report the go/no-go on the 50 real held-out.

• Grow it on real traffic post-launch (OP-DE06) — pilot usage is the cheapest

source of new labeled examples.

结果: A 50-example human-labeled, sealed held-out set built from the hardest

real contracts predicts production far better than 200 synthetic questions or any

public legal benchmark. The synthetic set still earns its keep — as the dev set

you tune against, never as the number you trust.

可提取的操作: OP-DE01, OP-DE02, OP-DE03, OP-DE04. **Lesson: spend scarce

labels on a small REAL held-out set; let synthetic generation cover the dev set;

never let a public bench be the gate.**

Dilemma 2 — "Our eval set went stale; scores are green but production is on fire"

困境: A support-triage classifier shows 0.91 on eval_v1 (built 9 months ago)

and every PR passes [[agentsop-regression-gate]]. Yet production accuracy collapsed and

users are escalating. The eval set says everything is fine.

约束: eval_v1 is versioned and trusted; nobody wants to "move the goalposts".

The domain shifted — a new product line generates a third of current tickets, and

none of those ticket types existed when eval_v1 was built. Rebuilding costs

annotator time.

决策步骤:

• Diagnose drift, not regression. Slice production traffic by ticket type and

compare against eval_v1's segment counts. The new product line is ~33% of live

traffic and 0% of the eval set → the eval set no longer represents the

domain. The green score is measuring an obsolete distribution.

• Do NOT just lower the threshold — the metric isn't wrong, the *data* is

stale. (Compare metric-design AP-8: changing the yardstick mid-stream without

re-grounding.)

• Refresh (OP-DE06): sample recent real tickets — especially the new product

line and recent escalations — label them, retire ticket types that no longer

occur, and build eval_v2.

• Bump the version (OP-DE05), keep eval_v1 for back-comparison. Re-score the

current system on eval_v2: it drops to 0.63 — now matching reality.

• Re-gate [[agentsop-regression-gate]] on eval_v2. Add a drift check to the

refresh cadence: quarterly, compare live segment mix vs eval segment mix; if any

segment drifts >X%, trigger a refresh.

结果: The "green-but-on-fire" gap was a stale held-out set, not a model

regression. A versioned refresh (eval_v2) restored the eval as a true production

predictor; the back-comparison against eval_v1 documented exactly how much the

domain moved.

可提取的操作: OP-DE06 RefreshOnDomainShift, OP-DE05 VersionTheSet. **Lesson:

a held-out set is a snapshot of a moving distribution. Schedule drift checks; an

old green score can be the most dangerous number you have.**

6. 反模式与边界 (Anti-Patterns & Boundaries)

Anti-patterns

| # | Anti-pattern | Why it's wrong | Fix |

|---|---|---|---|

| AP-1 | Public bench as proxy for domain performance ("92% MMLU → ship it") | Public benches measure *general capability*; near-uncorrelated with task fit past a floor | Build a domain held-out set; gate on it (OP-DE08) |

| AP-2 | Eval set leaks into prompt / training / trainset | Held-out number is inflated and meaningless; you're testing on the train set | Seal it; leak-audit before release (OP-DE03, OP-DE07) |

| AP-3 | Set too small to be significant (<30 examples) | "Memorizing, not training" [dspy.ai/learn/optimization/overview/]; variance swamps signal | Target 50–200 (OP-DE04) |

| AP-4 | Synthetic-only held-out (auto-generated QA *is* the test set) | Tests "what the corpus says", not real user phrasing; flatters the system | Synthetic = dev set bootstrap only; real-labeled = held-out (§7, Dilemma 1) |

| AP-5 | Never refreshing as the domain drifts | Green scores on an obsolete distribution; "green but on fire" (Dilemma 2) | Schedule drift checks; refresh + version (OP-DE06) |

| AP-6 | Unversioned set silently edited | Can't compare across system versions; results table is meaningless | Hash + date + rubric; bump on deliberate refresh (OP-DE05) |

| AP-7 | No stratification / edge cases (only easy head-of-distribution) | Passes eval, fails the tail where systems actually break | Stratify by segment/type; over-sample edge cases (OP-DE01) |

| AP-8 | Tuning chunk size / reranker / model against the held-out set | That makes it a val set, not held-out; the trust is gone | Tune on dev; touch held-out only at decision time (OP-DE03) |

Boundaries (when this skill is the wrong tool)

• You only need general capability comparison / academic reporting. "Which

model is best at reasoning?" → [[lm-evaluation-harness]] (MMLU/GSM8K/etc.),

not this skill. This skill is for *your* task, not the leaderboard.

• An objective oracle already exists (compiler passes, exact DB match, schema

validity gives ≥95% of signal). The "eval set" is just running the oracle on

inputs — you don't need curated human-labeled held-out examples. Don't

gold-plate.

• Nothing ships and no decision rides on quality (throwaway prototype). The

cost of building and labeling a real set has no payoff.

• You have zero access to real domain data and no path to any (pre-product,

cold start). Bootstrap with synthetic + public benches transparently, label

*as soon as* pilot traffic appears, and treat early numbers as provisional.

• Scoring each example is itself the hard part (open-ended generation, no

reference answer) — building the *set* is necessary but not sufficient. Pair

with [[agentsop-metric-design]] to define a defensible, calibrated scoring function.

7. 跨框架对照 (Cross-Framework Mapping)

When does each kind of eval set apply? They are complementary axes, not

substitutes — a mature pipeline uses all three.

| Concept | Held-out domain set (this skill) | [[lm-evaluation-harness]] (public) | LlamaIndex DatasetGenerator (synthetic) |

|---|---|---|---|

| What it measures | Task fit on *your* data | General capability | Coverage of *your corpus's* content |

| Data source | Real traffic, human-labeled | Public academic datasets (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) | LLM-generated QA from your docs |

| Size | 50–200 | thousands (fixed by benchmark) | arbitrary (default num=50) |

| Contamination risk | You control it (leak-audit) | High — public benches leak into pretraining | Low (your private corpus) but synthetic |

| Predicts production? | Yes (the decision gate) | No (capability floor / sanity check) | Partially (dev-set iteration, not the gate) |

| When to use | Go/no-go on shipping to *your* users; per-PR regression gate | Model selection on raw capability; academic reporting; training-progress tracking | Bootstrap a dev set fast before you've labeled real data |

| Invocation | eval_vN.jsonl + scoring fn from [[agentsop-metric-design]] | lm_eval --tasks mmlu,gsm8k,... | DatasetGenerator.from_documents(docs).generate_dataset_from_nodes(num=50) |

Decision rubric:

Q1. Are you deciding whether to SHIP / SWITCH on YOUR users' data?
    YES → held-out domain set is the gate (this skill). Public bench = sanity check only.
Q2. Are you comparing raw model capability or reporting academic numbers?
    YES → lm-evaluation-harness (MMLU/HumanEval/GSM8K). Not this skill.
Q3. Do you have NO real labeled data yet but a corpus exists?
    YES → DatasetGenerator to bootstrap a DEV set; label real held-out as soon as traffic appears.
Q4. Is there an objective oracle (tests/schema/exact-match)?
    YES → run the oracle; no curated set needed.
DEFAULT → build + version a 50-200 real held-out set; gate via [[agentsop-regression-gate]];
          score via [[agentsop-metric-design]]; pair with [[lm-evaluation-harness]] for the floor.

Combination patterns:

• this skill + [[agentsop-regression-gate]]: this skill *produces* the sealed,

versioned set; regression-gate *enforces* it on every PR (chunking / embedding /

prompt / model change). Division of labor: produce vs enforce.

• this skill + [[agentsop-metric-design]]: this skill defines *what's in the set*;

metric-design defines *how each example is scored* (decomposed sub-judges,

bool-during-compile/float-during-eval, human-calibrated, length-penalized).

A set with no defensible scoring function is half a benchmark.

• this skill + [[lm-evaluation-harness]]: report both axes side by side

(OP-DE08). Public bench answers "is the model competent?"; the domain set

answers "does it work for us?". On disagreement, the domain set wins go/no-go.

• this skill + DSPy: the held-out test split is the DSPy exit-gate set —

"compiled program beats baseline on a *held-out* test set (not the val set)"

[dspy.ai/learn/optimization/overview/]. The DSPy trainset/valset come from the

*non-held-out* splits.

Opinionated default: build the held-out set in plain jsonl (transparent,

diffable, hashable), label it with humans on the hardest real examples, seal it,

version it, and treat the public-benchmark number as a sanity check you report

but never gate on.

References

• references/R1-source-evidence.md — verbatim source quotes (DSPy held-out

discipline, LlamaIndex eval-loop, lm-evaluation-harness public scope)

• intermediate/operation_candidates.json — 8 operations in Trigger / Action /

Output / Evidence form

Cross-links: [[lm-evaluation-harness]] (public-benchmark axis),

[[agentsop-regression-gate]] (per-PR enforcement), [[agentsop-metric-design]] (scoring function).

Citations: [dspy.ai/learn/optimization/overview/], [dspy.ai/learn/optimization/optimizers/], [dspy.ai/learn/evaluation/metrics/], [dspy.ai/tutorials/saving/], [developers.llamaindex.ai/python/framework-api-reference/evaluation/], [llamaindex.ai/blog/evaluating-the-ideal-chunk-size-for-a-rag-system-using-llamaindex-6207e5d3fec5], ~/.claude/skills/lm-evaluation-harness/SKILL.md.