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.
Step one: Model output
Capture the raw JSON exactly as produced. Do not clean or rewrite it yet.
Step two: Structure check
Confirm that the JSON is valid and complete.
This is the first point where most evaluations explode.
Step three: Schema validation
Make sure every field is present, types are correct, and structure matches expectations.
This prevents silent failures caused by misplaced answers.
Step four: Scoring
Only after the JSON survives structure and schema checks should you compute scores.
Step five: 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.
But when we inspected the raw output, the reasoning was correct.
The answer was just placed in a field named result instead of answer.
Without schema validation the scoring script threw the output away.
This created an illusion of model degradation.
A simple schema step fixed the problem.
4. Tools and Patterns That Help
You can use any strict JSON schema validator.
The important part is that it runs before the scoring step, not after.
It should produce 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.
Top comments (0)