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Artificial intelligence, practical applications and emerging tools.
Slync planning guide
How do you evaluate an AI feature before release?
Define the task, collect representative examples and decide how success and failure will be judged. Compare the AI approach with the current process using the same examples. Release only within a scope where mistakes can be detected and handled.
What should your evaluation contain?
Include ordinary requests, ambiguous inputs and examples the system should decline or escalate. Keep evaluation examples separate from prompt-tuning examples. Record the model, prompt, data sources and configuration for each run.
How should you report the results?
Report the number and type of examples, the scoring method and the failures that remain. Assess task quality alongside latency and cost. Repeat the evaluation when the model, prompt or retrieval data changes; a convincing demo is not a reliability measurement.
Before you start
- Define one task and the unacceptable failure cases.
- Remove confidential data from development examples.
- Record a baseline and review errors with a person familiar with the task.
- Provide human review or escalation where an incorrect answer matters.
Reference: OpenAI: Working with evals. This planning guide provides general guidance, not a report of client results. Prepared with AI assistance; consult the linked documentation for product-specific requirements.
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