DOCUMENT AND DATA USE CASES
Analyze long documents, return validated structured data, or explain a verified calculation. Open the workflow that matches the required output.
Production recipe
| What to measure | What to record |
|---|---|
| Accuracy on your task set | Result, latency, token use, and reason for accepting or rejecting the output |
| Output consistency | Result, latency, token use, and reason for accepting or rejecting the output |
| Latency budget | Result, latency, token use, and reason for accepting or rejecting the output |
| Cost of retries and verification | Result, latency, token use, and reason for accepting or rejecting the output |
Analyze long documents, return validated structured data, or explain a verified calculation. Open the workflow that matches the required output.
Use known answers, missing and conflicting evidence, strict schemas, and deterministic checks. Accept a response only when the application can verify the required fields or result.
Include retries, tool calls, validation, escalation, and manual correction. Compare the total cost of results your application can actually accept, not token price alone.
Start with the highest-volume or highest-risk workflow for which you already have representative inputs and a clear definition of success.
Start with the recommended model and one fallback. Add another only if neither meets the required quality, latency, or cost boundary.
Use the same inputs, context, tools, output limits, validation rules, and stopping conditions for every model.
Compare a primary route and fallback using the same application inputs and validation rules.
