jev vs structured output

The JSON Can Look Similar While the Computation Differs

The first interface can extract or generate an arbitrary summary. The second represents a bounded competition among known candidates. A shared JSON envelope does not make the systems interchangeable.

// Schema-constrained generation may create a new value:
{"issue_type":"billing", "summary":"Duplicate charge after retry"}

// A Jev Choice selects among values supplied by the caller:
{"answer":"billing", "distribution":{"billing":0.82,"technical":0.13,"other":0.05}}

Choose from the Information the Output Must Contain

Schema Validity and Semantic Validity Are Separate

A schema-valid response can still choose the wrong label, invent an extracted value or cite unsupported evidence. Conversely, a correct free-text answer can fail parsing. Track invalid-output rate separately from semantic error so structured decoding does not appear to solve evaluation quality.

Jev’s typed contract reduces malformed-output handling, but the candidate list can be incomplete or overlapping. A Choice still selects among supplied candidates. Add an explicit no-match design and measure it; see Choice versus Noul.

Probability Access Is Not Equivalent Across Interfaces

Jev returns the answer distribution directly. A schema-constrained LLM API may expose token log probabilities, repeated samples or a generated confidence field, but these are not automatically a calibrated probability over the business labels. Serialization tokens and label probabilities are different objects.

Compare calibration with blind labels using the actual signal available to each system. If the production action only needs a class, do not reward a generated confidence sentence for sounding certain.

Design the Failure Contract Before Comparing Accuracy

Typed syntax does not define operational semantics. Decide whether each failure becomes retry, review, fallback or refusal before running the benchmark, then score that complete behavior. The errors and retries guide provides a failure taxonomy, and abstention design separates uncertainty from missing evidence.

Generated Rationale Can Help and Can Mislead

Structured generation can return a label, evidence IDs and explanation together. That is useful when a person must understand or appeal the decision. Validate that cited evidence exists and supports the explanation; generated rationales can be plausible after-the-fact stories.

Jev does not expose reasoning. Pair its result with source evidence selected by deterministic code or use another model for explanation, clearly labeling which component produced which artifact.

Run the Identical Classification Both Ways

The evaluator comparison guide provides the full protocol. Do not infer a winner from architecture alone.

1. Freeze the labels, evidence and human adjudication before model runs.
2. Give both systems equivalent criterion meaning using their native interfaces.
3. Pin model, provider, schema, question or prompt, and retry behavior.
4. Measure class errors, calibration signal, invalid outputs and repeated-run stability.
5. Measure end-to-end latency, tokens, fallbacks and reviewer time.
6. Report results by slice and retain raw artifacts for audit.

Migration Requires an Adapter and a New Threshold Study

When replacing a structured-output LLM with Jev, move generated enums to Choice, independent Boolean fields to separate Nouls and genuinely ordered rubrics to Score. Keep arbitrary strings, explanations and unknown-value extraction in a generative or deterministic stage. Do not squeeze every existing JSON field into a Jev question simply to preserve one call.

Normalize both systems into a shared decision record while retaining their native outputs. Run paired examples, inspect disagreements, fit each system’s thresholds separately and compare at equal automation coverage. The primitive guide maps answer shapes, while regression testing defines promotion and rollback fixtures.