AI Prompt Engineering · Lesson

Error Handling in Prompt Chains

Validating intermediate outputs and recovering from chain failures.

Why Chains Fail

Prompt chains introduce new failure modes that single-prompt systems do not have. Each step can fail in its own way, and failures compound — a bad Step 2 output corrupts every downstream step.

Common failure modes:

Model returns malformed JSON that cannot be parsed
Model misunderstands the task and produces semantically wrong output
Rate limits or API timeouts cause step failures
Context window exceeded in a long chain
Model hallucinates data that subsequent steps treat as fact

Output Validation After Every Step

The first line of defense is validating output immediately after each step before passing it to the next. Never assume the model returned what you asked for.

import json

def validate_json_output(raw_text, required_fields):
    'Parse and validate that required fields are present in model output.'
    try:
        data = json.loads(raw_text.strip())
    except json.JSONDecodeError as e:
        raise ValueError(f'Invalid JSON: {e}. Raw: {raw_text[:200]}')

    missing = [f for f in required_fields if f not in data]
    if missing:
        raise ValueError(f'Missing required fields: {missing}. Got: {list(data.keys())}')

    return data

# Usage after a chain step
raw = '{"sentiment": "positive", "priority": "high"}'
validated = validate_json_output(raw, required_fields=['sentiment', 'priority'])
print('Valid:', validated)

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