Build LLM Evals
This is how to monitor the performance of your AI systems
- How do you know your AI application is working well?
- How do you compare different models, prompts and retrieval setups?
- How do you systematically improve the performance of your entire AI system?
This is what I cover in a multi-part series on LLM Evals.
The multi-repo bundle (see Trelis.com for more details) now includes ADVANCED-fine-tuning, ADVANCED-inference, ADVANCED-transcription (incl. speech to text and text to speech), ADVANCED-vision (includes multi-modal and diffusion models), and now ADVANCED-evals.
Those who have already purchased the Trelis Multi-Repo bundle have gained free access to the ADVANCED-evals repo. Check your Github activity page!
Cheers, Ronan
Building Effective LLM Evaluation Systems
AI Summary
Trelis presents a systematic approach to evaluating Large Language Model (LLM) systems, demonstrating how to build evaluation frameworks from the ground up. The presentation uses a touch rugby rules assistant as a working example.
Core Evaluation Components
The tutorial begins by establishing four fundamental components of LLM evaluation: goals, pipelines, evaluation datasets, and grading approaches. These components form the backbone of any robust evaluation system, whether for ChatGPT, Claude, or Gemini implementations.
The primary goals of evaluation are threefold:
1. Determining if an LLM system works at all
2. Comparing performance across different approaches
3. Identifying opportunities for systematic improvement
Pipeline Architecture
Trelis demonstrates how to create modular pipelines that can serve both production and evaluation purposes. This approach ensures that what's being evaluated matches exactly what's running in production.
The pipeline implementation shown includes:
- Model selection (OpenAI, Anthropic, or Google)
- Temperature settings
- System prompts
- Response handling
Later, RAG or agentic tools can be added to create more complex pipelines.
Grading Methodologies
Two primary grading approaches are presented:
1. Ground truth-based grading for straightforward factual responses
2. Criteria-based grading for more nuanced answers
The presentation makes the case for criteria-based grading in production applications, where responses often require more sophisticated evaluation than simple matching.
Practical Implementation
Using PostgreSQL for data storage, the tutorial demonstrates how to:
- Set up evaluation infrastructure
- Create and modify evaluation datasets
- Run comparisons across different LLM models
- Track and analyze performance metrics
Model Comparison and Optimization
The tutorial concludes with a practical demonstration of comparing different LLM models (GPT-4o-mini, Claude, Gemini) on the same evaluation dataset. This comparison provides concrete insights into model selection and system prompt optimization.
Key Technical Insights
Throughout the presentation, several crucial technical points emerge:
- The importance of separating pipeline logic from application logic
- Methods for logging and analyzing model responses
- Techniques for iterative improvement of evaluation criteria
- Approaches to automated grading using LLMs themselves
Practical Applications
The methodology presented is particularly valuable for:
- AI development teams building production systems
- Quality assurance engineers working with LLMs
- Product managers needing to compare different AI solutions
- Developers implementing automated evaluation systems
Future Developments
The tutorial hints at future topics, including:
- Integration with RAG systems
- Using production data to enhance evaluation datasets
- Implementing human feedback loops
- Fine-tuning models based on evaluation results
This comprehensive guide provides a solid foundation for anyone working on LLM system evaluation, offering both theoretical understanding and practical implementation details. The measured, technical approach makes it particularly useful for AI engineers working on production systems.
The complete implementation, including code and configuration files, is available through the Trelis Advanced Evals repository, part of a broader suite of professional AI development tools and frameworks.