AI Prompt Engineering · Lesson

Repair and Validation Loops

Fixing malformed output.

Why Repair Loops Exist

Even with schemas, output can fail: truncated JSON from a token cap, hallucinated extra fields, a string where a number was required, or a violated business rule. A repair loop detects the failure and feeds it back to the model to correct.

It is the safety net that turns a 98% success rate into a 99.9% one.

The Validate-Repair Cycle

The canonical loop: generate, validate, and if invalid, re-prompt with the specific error, up to a bounded number of attempts.

def generate_valid(prompt, schema, max_tries=3):
    msgs = [{'role': 'user', 'content': prompt}]
    for _ in range(max_tries):
        out = call_model(msgs)
        ok, err = validate(out, schema)
        if ok:
            return out
        msgs.append({'role': 'assistant', 'content': out})
        msgs.append({'role': 'user',
                     'content': 'Invalid. Fix this error and resend JSON only: ' + err})
    raise ValueError('repair budget exhausted')

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