JSON Eval Failures: Why Evaluations Blow Up and How to Fix Them

Source: Dev.to

Evaluation pipelines for RAG and agent systems look simple on the surface.

• Model produces JSON.
• You parse the JSON.
• You score the output.
• Then you aggregate results.

In reality this is one of the most fragile parts of the workflow. A single misplaced field or formatting slip can make the entire evaluation unreliable. This guide explains why JSON evaluation fails and how to build a stable validation flow that prevents silent errors.

1. Why JSON Causes Evaluation Collapse

LLMs often generate partial structure. Fields get renamed. Objects become arrays for one sample and objects for another. A missing bracket can break the entire scoring script. When this happens the scoring step becomes meaningless—instead of measuring model quality you end up measuring formatting noise.

2. The Failure Flow Most Teams Miss

A stable evaluation pipeline needs four steps:

1. Model output – Capture the raw JSON exactly as produced. Do not clean or rewrite it yet.
2. Structure check – Confirm that the JSON is valid and complete. This is the first point where most evaluations explode.
3. Schema validation – Make sure every field is present, types are correct, and structure matches expectations. This prevents silent failures caused by misplaced answers.
4. Scoring – Only after the JSON survives structure and schema checks should you compute scores.
5. Aggregated report – A clean score report is only possible when earlier steps are stable.

3. A Real Example of JSON Eval Failure

We once had an evaluation batch where accuracy dropped dramatically. The model seemed to regress overnight. When we inspected the raw output, the reasoning was correct, but the answer was placed in a field named result instead of answer. Without schema validation the scoring script threw the output away, creating an illusion of model degradation. Adding a simple schema step fixed the problem.

4. Tools and Patterns That Help

• Use any strict JSON schema validator.
• Run the validator before the scoring step, not after.
• Ensure it produces a clear error report so you know when the model failed structurally rather than semantically.

5. Takeaway

If your evaluations feel unstable, it is probably not the model—it is the JSON. Add structure checks and schema validation before scoring, and you will get predictable evaluations every time.