Configure advanced agent responses with custom prompts
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Included in this chapter:
- What a custom prompt is, and which surface owns what
- Where the prompt runs, and who picks the moment
- Writing the instruction: the task, the context, and a way out
- Inputs: text, image or document, and Power Fx
- Grounding a prompt in your own business data
- The 100-second budget and what fills it
- Choosing the model, and what a model choice commits you to
- The settings panel: temperature, moderation, and code interpreter
- Giving the response a shape downstream logic can bind to
- Testing the prompt, then testing the agent around it
- Where a rule belongs: the prompt, the agent, or a topic
- How these choices show up in questions
Prompt model categories, as Microsoft groups them
| Consideration | Mini | General | Deep |
|---|---|---|---|
| Performance | Good for most tasks | Superior for complex tasks | Trained for reasoning tasks |
| Speed | Faster processing | Might be slower due to complexity | Slower, as it reasons before responding |
| Published use cases | Summarization, information tasks, image and document processing | Image and document processing, advanced content creation tasks | Data analysis and reasoning tasks, image and document processing |
| Billing rate of the listed models | Basic rate | Standard rate | Premium rate |
| Pick it when | The task is bounded and latency or cost matters | The task is complex or multimodal and accuracy matters more than speed | The task genuinely needs multi-step reasoning |
Decision tree
Cheat sheet
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Also tested in
References
- Prompts overview
- Prompt assistant
- Create a prompt
- Use prompts to make your agent or agent flow perform specific tasks
- Add tools to custom agents
- Prompts performance and execution
- Get started with prompt library
- Add inputs to your prompt
- FAQ for prompts FAQ
- Add knowledge to your prompt
- Change the model version and settings
- Write agent instructions
- Prompt model availability by region and updates
- Bring your own model for your prompts
- Use code interpreter in a prompt to generate and execute Python code
- JSON output
- Test your agent - Microsoft Copilot Studio
- Configure high-quality instructions for generative orchestration