Structured-Output-Picker — 在解码处约束，还是在校验处约束？

> One-liner: Three local libraries (Outlines, Instructor, Guidance) plus > provider-native structured outputs all "make the model emit valid structure", > but they enforce at different points and fail differently. Pick by how > costly a malformed output is and whether you control the decoder. Then > pick the failure stance — Assert (hard-fail + retry) vs Suggest (soft > nudge, degrade gracefully) — borrowed from DSPy's constraint primitives.

This is an ENHANCE overlay. The four enforcement mechanisms each have a working local skill; what no single one provides is the cross-library which-one + how-to-handle-failure decision. That gap is hit every time an LM output is consumed by code. For what shape the content should take (code vs JSON vs prose), descend first to [[agentsop-output-format-by-model]] — this skill assumes the shape is already chosen and asks only how to enforce it.

1. 何时激活 (When to activate)

Activate after you have decided the output shape (via [[agentsop-output-format-by-model]]) and the answer was "a typed/validated object", and before you write the parsing code.

| Trigger | Signal | |---|---| | LM output feeds a parser | json.loads(resp) / Model.model_validate(...) is in the next line of code | | You picked a typed shape | format-by-model said "JSON / Pydantic / typed field" — now: who enforces it? | | Repeated parse failures | JSONDecodeError, ValidationError, truncated/extra-prose responses in logs | | Library is already installed | outlines, instructor, or guidance is in the env and you must choose between them | | An enum / regex / range must hold | output must be one of N labels, a valid date, a bounded int | | You must decide failure stance | "if the model returns garbage, do I crash, retry, or accept-and-flag?" |

Anti-triggers (skip this skill):

• The content is code / multi-step reasoning / long prose — go back to

[[agentsop-output-format-by-model]]; enforcing a JSON grammar on code is the headline anti-pattern there (Aider 61%→20%). Enforcement strength is the wrong question when the shape is wrong.

• No code consumes the output yet (one-shot exploration).
• The shape is one token / one number — any parser works; no library needed.

2. 核心心智模型 (Core mental model)

2.1 The axis: where is the constraint applied?

   PROMPT ──────► DECODE ──────► RAW TEXT ──────► VALIDATE ──────► TYPED OBJECT
     │              │                                │
     │         constraint at DECODE             constraint at VALIDATE
     │         (grammar masks tokens)           (parse, check, retry on fail)
     │              │                                │
  ask nicely    Outlines / Guidance /          Instructor / DSPy Suggest /
  (weakest)     provider strict-mode           hand-rolled retry loop
                  ↑                                  ↑
            CANNOT emit invalid               CAN emit invalid, then
            structure — masked at             catches it and re-asks
            the logit level                   with the error injected

The pick is governed by one question: how costly is a malformed output?

• A malformed output is cheap to recover from (one extra API round-trip is

fine; the model is strong; failures are rare) → constrain at validate (Instructor-style retry). Simpler, model writes more naturally, no decoder access needed.

• A malformed output is expensive or impossible to recover from (no retry

budget, hard real-time, the output must be in a fixed enum/grammar, a single bad token corrupts a batch job) → constrain at decode (Outlines / Guidance grammar, or provider strict-mode). The model cannot emit invalid structure.

2.2 Two prerequisites that gate the choice

1. Do you control the decoder? Grammar/token-masking (Outlines, Guidance)

requires logit access — i.e. local/open weights (Transformers, vLLM, llama.cpp) outlines]] guidance]]. Closed API models (GPT, Claude) expose only their own native structured-output / tool_use; you cannot bolt Outlines onto them. Instructor works on top of the API by parse-and-retry instructor]].

1. Is the content code-shaped? If yes, stop — see §1 anti-triggers and

[[agentsop-output-format-by-model]]. Grammar-constraining code yields valid JSON containing degraded code: enforcement cannot buy back content quality.

2.3 Failure stance is orthogonal — Assert vs Suggest

Independent of which library, you choose what happens on a constraint violation. DSPy names the two stances dspy-sop-skill §Constraint primitives; arxiv.org/pdf/2312.13382]:

| Stance | Behavior on violation | Use when | |---|---|---| | Assert (hard) | retry up to N; then raise / halt | Dev-time bug-catching; downstream cannot tolerate a bad value; correctness > availability | | Suggest (soft) | retry with error injected; then log + continue with best effort | Production; partial result beats no result; availability > strict correctness |

> Decode-time grammar (Outlines/Guidance) is itself a hard guarantee on > shape — but semantic checks (range, cross-field, business rules) still need > an Assert/Suggest stance layered on top.

3. SOP 工作流 (Decision workflow)

Three ordered steps. Each presupposes [[agentsop-output-format-by-model]] already said "typed/validated object".

Step 1 — Decide enforcement strength (how costly is malformed?)

malformed output cost?
   │
   ├── cheap to recover (retry OK, strong model, rare failures)
   │        └──► VALIDATE-time enforcement   (weakest sufficient — prefer this)
   │
   ├── must never escape (enum/regex/grammar is a hard contract)
   │        └──► DECODE-time grammar          (only if you control the decoder)
   │
   └── no retry budget / hard real-time / batch where 1 bad token poisons many
            └──► DECODE-time grammar          (or provider strict-mode if API)

Heuristic: start at the weakest layer that meets the cost constraint. Validate + retry is cheaper to build, keeps the model's generation natural, and needs no decoder access. Escalate to decode-time grammar only when retry is too costly or the contract is absolute.

Step 2 — Pick the library (gated by decoder access + provider)

| You have | Output goes to | Pick | |---|---|---| | Closed API model (GPT/Claude) + Pydantic schema | typed object, retries acceptable | Instructor — validate-time, Pydantic + auto-retry instructor]] | | Closed API model + provider supports native | typed object, want zero extra deps | provider native structured outputs / tool_use (then Instructor or hand-parse on top) | | Local/open weights + must guarantee JSON/regex/enum | hard contract, no retry budget | Outlines — decode-time grammar/regex/JSON-schema outlines]] | | Local/open weights + interleaved gen + control flow | multi-step / fill-in-the-middle / mixed text+constrained | Guidance — decode-time + Pythonic control flow guidance]] | | Any model + only semantic checks needed (shape already valid) | range / cross-field / business rules | retry loop with Assert/Suggest stance (DSPy or hand-rolled) |

Step 3 — Choose the failure stance (Assert vs Suggest)

• Default Suggest in production (degrade gracefully; log the violation).
• Use Assert in dev/test and on values whose corruption is unacceptable

downstream (IDs that index a DB, money amounts, irreversible actions).

• Decode-time grammar covers shape for free; still wrap semantic checks in a

stance. Set a finite retry cap on either stance — an unbounded retry loop is an outage.

4. 操作模型 (Picker table — task → mechanism + stance)

| # | Task | Mechanism | Stance | Rationale / Evidence | |---|---|---|---|---| | 1 | Extract invoice fields from GPT-4o, retry on miss | Instructor (Pydantic + max_retries) | Suggest | API model → validate-time; auto-retry on ValidationError instructor]] | | 2 | Classify into a fixed 4-label enum on a local Llama | Outlines regex/choice | Assert | enum is a hard contract; one masked decode guarantees it outlines]] | | 3 | Local model must emit schema-valid JSON, no retry budget (batch) | Outlines JSON-schema | Suggest (log shape always holds) | decode-time guarantee; a bad token in a 100k batch is too costly to catch later outlines]] | | 4 | Interleave reasoning text + a constrained {action, arg} block, local | Guidance (control flow + grammar) | Suggest | Guidance interleaves free text and constrained spans in one program guidance]] | | 5 | Closed API, want typed output with zero new deps | provider native structured outputs / tool_use | Assert on parse | provider strict-mode guarantees schema validity (not content quality) | | 6 | Output shape already valid; need 0 <= score <= 1 and start < end | retry loop, no grammar | Assert (dev) / Suggest (prod) | semantic, not shape — grammar can't express cross-field; needs a check + stance dspy 2312.13382] | | 7 | Local model, must match a date/email regex | Outlines regex | Assert | regex is a decode-time native; cheaper than parse-and-retry outlines]] | | 8 | Streaming partial Pydantic objects from an API as they arrive | Instructor streaming | Suggest | Instructor streams partial validated objects instructor]] |

5. 困境决策案例 (Dilemma cases / worked examples)

Case A — "Instructor keeps retrying and burning tokens; should I switch to Outlines?"

Trigger: An extraction service on GPT-4o uses Instructor with max_retries=5. Latency p95 spiked; logs show 15% of calls retry ≥2× on the same nested schema.

Constraints:

• Closed API model — no decoder access, so Outlines is not available for

GPT-4o outlines]] instructor]].

• The schema is deeply nested with several free-text fields.
• The cost being paid is retries, i.e. malformed output is currently expensive.

Decision steps:

1. Outlines is off the table for an API model (Step-1 prerequisite §2.2(1)). The

real lever is reducing the validate-time failure rate.

1. Inspect what fails. If the model emits valid JSON but wrong content, no

enforcement layer helps — that's a prompt/format problem; check whether a free-text field is being squeezed into JSON ([[agentsop-output-format-by-model]] mixed-content trap).

1. If the failure is shape (extra prose, truncation): switch the API call to

provider native structured outputs / tool_use (strict-mode guarantees schema validity at the API), then keep Instructor only for the Pydantic typing layer. Retries collapse because shape is now guaranteed upstream.

1. Lower max_retries to 2 and flip the stance to Suggest with a logged

fallback object, so a stubborn case degrades instead of inflating p95.

Outcome: Don't "switch to Outlines" (impossible here). Move shape-enforcement to the provider's native layer, keep Instructor for typing, cap retries, Suggest.

Case B — "Local model, 200k-row batch extraction; a few rows come back malformed"

Trigger: Nightly batch over a local vLLM-served model. ~0.3% of rows produce JSON that fails to parse, poisoning the downstream load.

Constraints:

• Local weights → decoder access available (Outlines/Guidance both viable).
• Batch job, no interactive retry budget — re-running the whole batch is the

only "retry", which is hugely expensive. Malformed output is very costly.

• The structure is a flat JSON schema; no interleaved free text.

Decision steps:

1. Step-1: malformed output is expensive and there is no per-row retry budget →

decode-time grammar. This is exactly the case validate-time loses.

1. Step-2: local weights + pure JSON shape + no interleaving → Outlines

JSON-schema. (Guidance would be the pick only if rows needed interleaved free-text + constrained spans, Case D-style.) outlines]] guidance]]

1. Shape is now guaranteed per row — the 0.3% parse failures go to zero by

construction. Stance for shape becomes moot.

1. Layer a Suggest semantic check (e.g. amount >= 0) and write violators to

a quarantine table rather than failing the batch — availability of the 99.7% beats halting on outliers.

Outcome: Decode-time Outlines grammar removes the shape failures the retry model couldn't afford to catch; a Suggest semantic check quarantines the rest.

6. 反模式与边界 (Anti-patterns & boundaries)

Anti-patterns

1. Grammar-constraining when a retry suffices. Reaching for Outlines/Guidance

on a strong API model with rare failures buys complexity (and may be impossible — no decoder access) when an Instructor retry would have been two lines. Start at the weakest sufficient layer (§3 Step 1).

1. Assert where Suggest is enough → over-rejection. Hard-failing the whole

request because one optional field violated a soft preference throws away a usable answer. In production, default to Suggest; reserve Assert for values whose corruption is unacceptable dspy 2312.13382].

1. Enforcing structure on code/prose content. The headline cross-skill

anti-pattern: a JSON grammar produces valid JSON containing degraded code. Enforcement cannot recover content quality — fix the shape first via [[agentsop-output-format-by-model]].

1. Assuming provider strict-mode fixes content. Native structured outputs

guarantee schema validity, not field correctness — same lesson as Aider's strict-mode JSON test agentsop-output-format-by-model]].

1. Unbounded retry loop. Validate-time enforcement without a finite cap turns

a flaky field into an availability outage. Always cap N.

1. Stacking Outlines on a closed API model. Token-masking needs logits;

GPT/Claude expose none. Mixing the mental models wastes a debugging cycle (§2.2(1)).

1. Using a grammar for cross-field/semantic rules. Grammars constrain token

shape, not relationships like start < end or "id exists in DB". Those need a validate-time check + stance, regardless of how shape was enforced.

1. Picking the library before deciding strength + stance. The library is the

third decision, after "how costly is malformed" and "Assert or Suggest".

Boundaries (when this skill doesn't apply)

• Output is code / reasoning / prose → [[agentsop-output-format-by-model]], not here.
• One token / one number / yes-no → any parser; no enforcement library.
• No code consumes the output → one-shot exploration, defer.
• Provider contract is fixed (must emit a specific webhook JSON) → enforcement

is mandatory by definition; the only remaining choice is the failure stance.

• The fix is prompt-level (model emits valid-but-wrong values) → no

enforcement layer helps; this is a prompt/format problem.

7. 跨框架对照 (Ecosystem cross-reference)

Where each mechanism sits on the decode vs validate axis, and what it guarantees. Cross-link [[agentsop-output-format-by-model]] for the prior shape decision.

| Mechanism | Constraint point | Needs decoder access? | Works on closed API? | Native failure model | Best for | |---|---|---|---|---|---| | Outlines outlines]] | Decode (token mask: regex / CFG / JSON-schema) | Yes (Transformers/vLLM/llama.cpp) | No | Cannot emit invalid shape — no failure to handle for shape | Local models; hard enum/regex/JSON guarantee; batch w/o retry budget | | Instructor instructor]] | Validate (parse → Pydantic → auto-retry) | No | Yes | Catches ValidationError, re-asks with error; streaming partials | API models; Pydantic typing + graceful retry | | Guidance guidance]] | Decode (grammar) + interleaved control flow | Yes | No | Constrained spans can't be invalid; free spans unconstrained | Local; interleaved text + constrained blocks; multi-step programs | | Provider native (OpenAI structured outputs / Anthropic tool_use) | Decode (provider strict-mode) | N/A (provider-side) | Yes (that provider only) | Schema-valid guaranteed; content quality not | API models; zero extra deps; tool-call args | | DSPy Assert/Suggest dspy 2312.13382] | Validate (assertion + backtrack) | No | Yes | Assert raises after N; Suggest logs + continues | The stance layer on top of any of the above |

            DECODE-TIME                         VALIDATE-TIME
   (guarantee shape, need logits)        (catch + retry, model-agnostic)
   ┌─────────────┬──────────────┐        ┌──────────────┬─────────────┐
   │  Outlines   │   Guidance   │        │  Instructor  │ DSPy Assert/ │
   │ (regex/CFG/ │ (grammar +   │        │ (Pydantic +  │   Suggest    │
   │  JSON-sch.) │  control flow│        │  auto-retry) │ (stance)     │
   └─────────────┴──────────────┘        └──────────────┴─────────────┘
   ┌────────────────────────────┐
   │ Provider native strict-mode│  (decode-time, but provider-side; API only)
   └────────────────────────────┘

How the two skills compose:

[[agentsop-output-format-by-model]]  →  decides the SHAPE   (code? JSON? prose? typed?)
                                       │
                  shape == "typed/validated object"
                                       ▼
[[agentsop-structured-output-picker]] (this) →  decides ENFORCEMENT
                                       │
              Step1 strength → Step2 library → Step3 Assert/Suggest

[[agentsop-output-format-by-model]] already lists Outlines/Guidance under "grammar libs" and provider tool_use under its §7 — it says choose the format first, then let a lower layer enforce it. This skill is that lower layer made into a decision.

Quick reference card

┌────────────────────────────────────────────────────────────────────┐
│                  STRUCTURED-OUTPUT ENFORCEMENT CARD                 │
├────────────────────────────────────────────────────────────────────┤
│ 0. Shape already "typed object"? If not → [[agentsop-output-format-by-model]]│
│ 1. How costly is a malformed output?                               │
│      cheap to recover  → VALIDATE-time (Instructor / retry)        │
│      must never escape → DECODE-time grammar (need decoder access) │
│ 2. Pick library:                                                   │
│      API model + Pydantic + retry ok   → Instructor                │
│      API model, zero deps              → provider native           │
│      local + hard enum/regex/JSON      → Outlines                  │
│      local + interleaved text+constr.  → Guidance                  │
│      only semantic/cross-field checks  → retry loop + stance       │
│ 3. Failure stance:                                                 │
│      prod default → Suggest (log + continue, capped retries)       │
│      dev / unrecoverable value → Assert (raise after N)            │
├────────────────────────────────────────────────────────────────────┤
│ NEVER:                                                             │
│  • Bolt Outlines/Guidance onto a closed API model (no logits)      │
│  • Grammar-constrain code/prose (valid JSON, degraded content)     │
│  • Assume strict-mode fixes content correctness                    │
│  • Run an uncapped retry loop                                      │
│  • Assert where Suggest suffices (over-rejection)                  │
└────────────────────────────────────────────────────────────────────┘

引用源 (Citations)

Source lib skills (local):

• [[outlines]] — decode-time grammar/regex/JSON-schema; local models

(Transformers/vLLM/llama.cpp); ~/.claude/skills/outlines/SKILL.md.

• [[instructor]] — validate-time Pydantic + auto-retry + streaming partials;

OpenAI/Anthropic; ~/.claude/skills/instructor/SKILL.md.

• [[guidance]] — decode-time grammar + Pythonic multi-step control flow; local;

~/.claude/skills/guidance/SKILL.md.

Constraint-stance source:

• DSPy Assert vs Suggest — dspy-sop-skill/SKILL.md §Constraint primitives;

DSPy Assertions paper arxiv.org/pdf/2312.13382]; dspy.ai/learn/programming/7-assertions/].

Sibling Phase-D skill (cross-link, not duplicated):

• [[agentsop-output-format-by-model]] — d-output-format-by-model-skill/SKILL.md. Decides

the shape; this skill decides enforcement. Anchors the "strict-mode ≠ content quality" and "don't grammar-constrain code" claims (Aider code-in-JSON 61%→20%).

Provider docs (named, re-verify before pasting code, May 2026):

• OpenAI structured outputs: platform.openai.com/docs/guides/structured-outputs].
• Anthropic tool_use: docs.anthropic.com/en/docs/agents-and-tools/tool-use].

All source SKILLs read on 2026-05-20. This overlay introduces no API absent from the sources; see references/R1-source-evidence.md.