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

# Common Workflows

> Learn the most effective ways to use Sageloop for your specific needs

Learn the most effective ways to use Sageloop for your specific needs.

## Workflow 1: Evaluating a New Feature

**Time**: 30 minutes | **Goal**: Define quality for a new AI feature

### Steps

1. **Create Project** (2 min)
   * Name: "Feature Name - Evaluation"
   * Write initial system prompt based on requirements

2. **Add Scenarios** (5 min)
   * 15-20 real examples from product spec
   * Include edge cases and boundary conditions

3. **Generate & Rate** (15 min)
   * Generate outputs
   * Rate all outputs (5 min)
   * Add feedback on low ratings

4. **Extract Patterns** (2 min)
   * Run extraction
   * Review quality patterns

5. **Document Findings** (5 min)
   * Export golden examples
   * Share with engineering team

### Output

Clear quality definition ready for implementation.

***

## Workflow 2: Iterating on Existing Prompt

**Time**: 20 minutes per iteration | **Goal**: Improve underperforming feature

### Steps

1. **Rate Current Outputs** (5 min)
   * Open existing project
   * Rate all outputs if not already done

2. **Extract Patterns** (2 min)
   * Identify failure clusters
   * Review root causes

3. **Apply Suggested Fix** (3 min)
   * Click "Apply Fix & Retest"
   * Review prompt changes
   * Confirm update

4. **Rate New Outputs** (5 min)
   * Rate regenerated scenarios
   * Check if quality improved

5. **Iterate or Ship** (5 min)
   * If `>90%` success: Export and ship
   * If `<90%`: Repeat steps 2-5

### Expected Result

Success rate improves 10-20% per iteration.

***

## Workflow 3: Collaborative Evaluation

**Time**: Ongoing | **Goal**: Team alignment on quality standards

### Setup

1. Create project (PM)
2. Add scenarios (PM or team)
3. Generate outputs (PM)
4. Share project with team (PM)
5. Each team member rates independently (all)

### Why It Works

* Multiple perspectives on quality
* Discover subjective vs. objective failures
* Align team on standards

### Deliverable

* Consensus on quality definition
* Points of disagreement documented
* Clear behavioral spec

***

## Workflow 4: Comparing Two Models

**Time**: 30 minutes | **Goal**: Decide between GPT-4 vs Claude

### Steps

1. **Create Two Projects**
   * Project A: GPT-4
   * Project B: Claude
   * Same system prompt and scenarios

2. **Generate Outputs** (3 min)
   * Run Generation on both

3. **Rate Independently** (15 min)
   * Rate Project A outputs
   * Rate Project B outputs

4. **Compare Results** (5 min)
   * Check success rates
   * Read actual outputs
   * Assess quality differences

5. **Document Decision** (7 min)
   * Model choice
   * Success rate difference
   * Quality rationale

### Quick Decision Matrix

| Metric      | GPT-4  | Claude |
| ----------- | ------ | ------ |
| Quality (%) | 92%    | 88%    |
| Speed       | Slower | Faster |

**Decision**: Choose based on quality needs and performance trade-offs.

***

## Workflow 5: Export for CI/CD Integration

**Time**: 10 minutes | **Goal**: Get test suite into engineering workflow

### Prerequisites

* Achieved `>90%` success rate
* Have golden examples and failure patterns

### Steps

1. **Go to Insights**
2. Click **"Export"**
3. Choose **"Test Suite (pytest)"**
4. Download JSON
5. Share with engineering

### Engineering Integration

Engineers can now run:

```bash theme={null}
pytest sageloop_tests.json
```

Automatically tests AI behavior in CI/CD pipeline.

***

## Workflow 6: Testing Multiple Variations

**Time**: 40 minutes | **Goal**: A/B test different prompt versions

### Steps

1. **Create Base Project** (5 min)
   * Name: "Support Bot - Base"
   * Initial system prompt
   * Add 15 scenarios

2. **Generate & Rate Base** (15 min)
   * Generate outputs
   * Rate all outputs
   * Success rate: 70%

3. **Create Variation Project** (5 min)
   * Clone scenarios
   * Modify system prompt (e.g., "be more casual")
   * Generate

4. **Rate Variation** (10 min)
   * Rate new outputs
   * Compare success rates

5. **Choose Winner** (5 min)
   * Which variation performed better?
   * Update production prompt

### Example Variations

* **Version A**: Formal tone
* **Version B**: Casual tone
* **Version C**: Balanced

Results might show Version B gets higher ratings, so you'd use that.

***

## Quick Reference: Common Scenarios by Role

### Product Manager

1. **Daily**: Rate outputs as they're generated
2. **Weekly**: Run extraction to find patterns
3. **Monthly**: Export insights to engineering

### Design Lead

1. **Discovery**: Define tone and personality
2. **Evaluation**: Rate based on brand alignment
3. **Feedback**: Add feedback explaining brand misalignment

### Engineering Lead

1. **Review**: Examine extracted patterns
2. **Implement**: Build bot using specifications
3. **Test**: Run exported test suite in CI/CD

### Customer Support Lead

1. **Input**: Provide real support questions
2. **Rating**: Rate responses from customer perspective
3. **Feedback**: Explain what customers expect

***

## Tips for Smooth Workflows

**Naming Convention**: Use consistent project naming

```
Feature Name - Version Number - Date
Example: "Support Bot - v2 - Jan 2026"
```

**Feedback Standards**: Always explain 1-2 star ratings

```
❌ "Bad"
✅ "Too formal; customers expect casual, friendly tone"
```

**Rating Speed**: Use keyboard shortcuts for fast rating

```
Press 1-5 to rate, ↓ to next output = 5 min for 15 outputs
```

**Version History**: Keep all projects for comparison

```
Support Bot v1 (65% success)
Support Bot v2 (85% success)
Support Bot v3 (95% success)
```

**Team Sharing**: Export results, not just project links

```
Share PDF/JSON export for offline review
Easier to reference and discuss with stakeholders
```

***

## Troubleshooting Common Workflow Issues

### Issue: Patterns Not Found

**Cause**: Fewer than 15 rated outputs or all high ratings

**Fix**: Add more scenarios and rate them. Need minimum 15-30 ratings.

### Issue: Team Has Different Standards

**Cause**: Subjective quality definitions

**Fix**:

1. Compare ratings
2. Discuss disagreements
3. Document final standard
4. Re-rate together if needed

### Issue: Iterations Not Improving Quality

**Cause**: Root cause not properly addressed

**Fix**:

1. Review failure cluster reasoning
2. Make larger prompt changes
3. Add more specific instructions

***

## Next Steps

* [Back to Getting Started](/quickstart)
* [Master Rating](/guide/rating-outputs)