CHATBOT USE CASES
Build customer support, knowledge-base, or multilingual chat. Each workflow shows how to ground answers, handle missing information, and escalate safely.
Production recipe
| What to measure | What to record |
|---|---|
| Time to first token | Result, latency, token use, and reason for accepting or rejecting the output |
| Conversation-history cost | Result, latency, token use, and reason for accepting or rejecting the output |
| Instruction following | Result, latency, token use, and reason for accepting or rejecting the output |
| Fallback behavior | Result, latency, token use, and reason for accepting or rejecting the output |
Build customer support, knowledge-base, or multilingual chat. Each workflow shows how to ground answers, handle missing information, and escalate safely.
Include common questions, ambiguous requests, missing data, multi-turn context, and cases that must go to a person. Measure whether the final customer outcome is correct.
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.
