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Eval-Driven Development

Eval-Driven Development (EDD) is a systematic approach to improving AI assistant outputs through iterative evaluation and refinement. In TextLayer Core, EDD provides a structured framework for measuring, tracking, and enhancing the performance of your AI applications.

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What is Eval-Driven Development?

Eval-Driven Development is a methodology inspired by Test-Driven Development (TDD) but tailored for AI systems. While traditional software development has well-established testing methodologies, AI systems present unique challenges due to their probabilistic nature and the subjective quality of their outputs. The core principle of EDD is to:
  1. Establish evaluation criteria for your AI application’s outputs
  2. Create test datasets with representative examples
  3. Measure performance against these datasets
  4. Iterate on improvements based on evaluation results
  5. Track performance over time to ensure consistent progress
This cycle creates a feedback loop that drives continuous improvement in your AI applications.
Eval-Driven Development Cycle

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Why Use Eval-Driven Development?

AI systems often suffer from challenges that are difficult to detect through traditional testing methods:
  • Regression: Changes to prompts or models can cause unexpected performance drops in seemingly unrelated areas
  • Hallucination: Models may generate plausible-sounding but factually incorrect information
  • Consistency: Performance may vary across different input types or edge cases
  • Alignment: Ensuring outputs align with human preferences and organizational values
Eval-Driven Development addresses these challenges by providing:
  • Objective metrics to track performance over time
  • Reproducible evaluations that can be run automatically
  • Early detection of regressions or unexpected behaviors
  • Documentation of improvement processes for regulatory compliance
  • Confidence in deploying AI systems to production

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Implementation in TextLayer Core

TextLayer Core provides built-in support for Eval-Driven Development through integration with Langfuse, enabling automated testing and evaluation of your AI applications.

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Prerequisites

Before implementing EDD with TextLayer Core, you’ll need:
  1. A TextLayer Core installation (see Installation Guide)
  2. Access to a Langfuse account for evaluation management
  3. Langfuse API credentials configured in your environment

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Setting Up Evaluation Infrastructure

  • Environment Setup
  • Creating Datasets
  • Setting Up Evaluators
First, configure your environment with the necessary Langfuse credentials:
# Add to your .env file
LANGFUSE_PUBLIC_KEY=your_public_key
LANGFUSE_SECRET_KEY=your_secret_key
LANGFUSE_HOST=https://cloud.langfuse.com

# Optional: Configure default test datasets
TEST_DATASETS=dataset1,dataset2,dataset3
This enables TextLayer Core to communicate with Langfuse for evaluation tracking.

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Running Evaluations

TextLayer Core provides a CLI command for running evaluations against your test datasets: 
# Run tests on a specific dataset
flask run-dataset-test my_dataset

# Run tests on multiple datasets
flask run-dataset-test dataset1 dataset2 dataset3

# Add a version tag to identify this test run
flask run-dataset-test my_dataset --run-version=v1.0

# Use datasets configured in app config (TEST_DATASETS)
flask run-dataset-test --use-config
This command:
  1. Retrieves the specified datasets from Langfuse
  2. Processes each test case through your application
  3. Logs the responses back to Langfuse for evaluation
  4. Associates runs with version tags for tracking progress

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Analyzing Results

After running evaluations, analyze the results in Langfuse:
  1. Navigate to Datasets in your Langfuse dashboard
  2. Select the dataset you ran tests on
  3. View the evaluation results, including:
    • LLM-as-judge scores
    • Performance trends over time
    • Detailed feedback on individual test cases
  4. Filter by version tags to compare different iterations
Langfuse Evaluation Dashboard

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The EDD Workflow

Implementing a robust EDD workflow involves several key steps:

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1. Identify Key Capabilities

Begin by identifying the core capabilities your AI application needs to support:
  • What tasks should your assistant perform?
  • What knowledge domains should it cover?
  • What failure modes or edge cases are critical to avoid?
Create a capability matrix that maps out these requirements, which will guide your evaluation strategy.

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2. Create Representative Datasets

For each capability, create datasets containing:
  • Typical examples representing common use cases
  • Edge cases that test the boundaries of the capability
  • Negative examples designed to trigger potential failure modes
  • Golden examples that you want your system to handle perfectly
Organizing datasets by capability allows you to track performance across different aspects of your application.

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3. Define Evaluation Criteria

Establish clear criteria for what constitutes good performance:
  • Accuracy: Does the response contain factually correct information?
  • Relevance: Does the response address the user’s query?
  • Safety: Does the response avoid harmful or inappropriate content?
  • Completeness: Does the response provide all necessary information?
  • Efficiency: Is the response concise and to the point?
These criteria can be implemented as LLM-as-judge evaluators in Langfuse.

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4. Establish Baselines

Run initial evaluations to establish baseline performance: 
# Establish baseline with version tag
flask run-dataset-test all_datasets --run-version=baseline
This baseline serves as a benchmark for measuring future improvements.

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5. Implement Improvements

Based on evaluation results, implement targeted improvements:
  • Adjust prompts to address specific issues
  • Add new tools or retrievals for knowledge gaps
  • Implement guardrails for safety issues
  • Update model parameters for better performance
Each change should target specific weaknesses identified in your evaluations.

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6. Measure and Iterate

After implementing changes, run evaluations again: 
# Run evaluations with new version tag
flask run-dataset-test all_datasets --run-version=v1.1
Compare results against your baseline and previous versions to ensure:
  • Targeted capabilities have improved
  • Other capabilities haven’t regressed
  • Overall performance is trending positively
Repeat this cycle of improvement, measuring, and iteration to continuously enhance your application.

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Best Practices

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Continuous Integration

Integrate evaluations into your CI/CD pipeline: 
# Example GitHub Actions workflow
name: Eval-Driven Development

on:
  push:
    branches: [ main, staging ]
  pull_request:
    branches: [ main ]

jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.12'
      - name: Install dependencies
        run: pip install -r requirements.txt
      - name: Run evaluations
        run: python -m flask run-dataset-test all_datasets --run-version=${{ github.sha }}
        env:
          LANGFUSE_PUBLIC_KEY: ${{ secrets.LANGFUSE_PUBLIC_KEY }}
          LANGFUSE_SECRET_KEY: ${{ secrets.LANGFUSE_SECRET_KEY }}
This ensures that every code change is automatically evaluated for potential regressions.

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Version Control for Datasets

Treat your evaluation datasets as code:
  • Store dataset definitions in version control
  • Review changes to datasets like code changes
  • Document the purpose and expected behavior of each dataset
  • Update datasets as your application requirements evolve

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Holistic Evaluation

Don’t rely solely on automated metrics:
  • Combine automated LLM-as-judge evaluations with human review
  • Include both quantitative metrics and qualitative assessments
  • Evaluate across multiple dimensions (accuracy, safety, user experience)
  • Consider using A/B testing with real users for critical improvements

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Documentation

Maintain thorough documentation of your EDD process:
  • Track changes to prompts, models, and configurations
  • Document the rationale behind each improvement
  • Keep a changelog of performance improvements
  • Record decision-making processes for future reference
This documentation is invaluable for knowledge transfer, regulatory compliance, and troubleshooting.

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Conclusion

Eval-Driven Development provides a systematic approach to improving AI applications over time. By implementing EDD with TextLayer Core’s Langfuse integration, you can build AI systems that continuously improve, maintain high quality standards, and adapt to changing requirements. For more information on specific evaluation techniques and metrics, refer to the Langfuse Documentation and explore example evaluators in the Langfuse marketplace.
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