> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getusertrace.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompt-Based Testing

> Test your prompts directly with LLM models

Test your system prompts across different models and scenarios without building a full agent. Perfect for prompt optimization and A/B testing.

## Getting Started

### 1. Configure Your Model

Choose your preferred LLM provider and model:

<AccordionGroup>
  <Accordion icon="openai" title="OpenAI Models">
    **Available Models:**

    • GPT-4o, GPT-4o mini
    • GPT-4 Turbo, GPT-4
    • GPT-3.5 Turbo
  </Accordion>

  <Accordion icon="brain" title="Anthropic Models">
    **Available Models:**

    • Claude 3.5 Sonnet
    • Claude 3 Opus, Claude 3 Sonnet
    • Claude 3 Haiku
  </Accordion>

  <Accordion icon="google" title="Google Gemini Models">
    **Available Models:**

    • Gemini 1.5 Pro
    • Gemini 1.5 Flash
    • Gemini 1.0 Pro
  </Accordion>
</AccordionGroup>

### 2. Input Your System Prompt

Enter your complete system prompt in the text area. This is what will be used to guide the model's behavior during testing.

**Example:**

```
You are a helpful customer service agent for a delivery company. 
Your goal is to resolve customer issues quickly and empathetically.

Guidelines:
1. Always ask for the order number first
2. Acknowledge the customer's frustration
3. Provide clear next steps
4. Offer compensation when appropriate

Keep responses under 100 words and maintain a professional tone.
```

<Warning>
  **Remove Temporary Variables**: Make sure to remove any placeholder text like `{customer_name}` or `{order_id}` from your prompt. Use static examples instead.
</Warning>

### 3. Configure Model Parameters

**Temperature (0.0 - 1.0)**

* **0.0-0.3**: Consistent, predictable responses
* **0.4-0.7**: Balanced creativity and consistency
* **0.8-1.0**: More creative and varied responses

**Response Format**

* **Text**: Standard text responses
* **JSON**: Structured JSON output (specify schema in prompt)

### 4. Set Evaluation Context

This is crucial for accurate testing. Add any information from your prompt that the evaluator needs to know:

**What to Include:**

* Key guidelines or rules from your prompt
* Expected response format or structure
* Specific goals or success criteria
* Any constraints or limitations

**Example Evaluation Context:**

```
The agent should:
- Always ask for order number first
- Acknowledge customer frustration
- Keep responses under 100 words
- Maintain professional tone
- Offer compensation when issues warrant it

Success criteria:
- Issue resolution within 3 exchanges
- Customer satisfaction maintained
- Company policies followed
```

<Info>
  **Why This Matters**: The simulated user and evaluator don't see your system prompt during testing. The evaluation context ensures they understand what behavior to expect and how to measure success.
</Info>

### 5. Advanced Features

**Tool Calls & Function Calling**

If your prompt involves tool calls or function calling, use our [Custom Chat Agent](/quickstarts/custom-chat-agent) setup instead, which provides full control over tool definitions and execution.

## Best Practices

<CardGroup cols={2}>
  <Card title="Prompt Clarity" icon="lightbulb">
    **Clear Instructions**

    • Use specific, actionable guidelines
    • Include examples of good responses
    • Define success criteria explicitly
  </Card>

  <Card title="Testing Strategy" icon="target">
    **Effective Testing**

    • Start with edge cases
    • Test across different user personas
    • Compare performance across models
  </Card>
</CardGroup>

## Example Workflow

1. **Input** your system prompt
2. **Select** OpenAI GPT-4o with temperature 0.3
3. **Add** evaluation context about expected behavior
4. **Choose** scenarios like "frustrated delivery customer"
5. **Run** simulations across multiple personas
6. **Analyze** results and iterate on your prompt

## Common Use Cases

* **Customer Service**: Testing support agent responses
* **Content Creation**: Evaluating writing assistant prompts
* **Educational**: Testing tutoring or explanation prompts
* **Classification**: Testing categorization and tagging prompts
* **Summarization**: Testing document or conversation summaries

<Note>
  **Ready to test more complex scenarios?** Check out our [Custom Chat Agent](/quickstarts/custom-chat-agent) guide for tool calling, multi-turn conversations, and custom API integrations.
</Note>