Building and Evaluating AI Systems: Insights and Best Practices

Building AI Systems

In the rapidly evolving field of artificial intelligence, building robust AI systems is a critical endeavor. Eugen Yan, a prominent figure in AI, has shared valuable insights on Patterns for Building LLM-based Systems, providing a comprehensive guide for engineers. His work emphasizes the importance of design patterns in machine learning code and systems, as detailed in his articles on Design Patterns in Machine Learning Code and Systems and More Design Patterns For Machine Learning Systems.

One of the key takeaways from Yan’s work is the principle of starting without machine learning, as outlined in his article The First Rule of Machine Learning: Start without Machine Learning. This approach encourages engineers to first understand the problem and data before diving into complex ML models. Additionally, Yan’s insights on System Design for Recommendations and Search offer practical advice for creating effective recommendation systems.

Evaluations and Testing

Evaluating AI systems is as crucial as building them. Yan has extensively covered this aspect in his writings. His article on Task-Specific LLM Evals that Do & Don’t Work provides a detailed analysis of evaluation techniques for large language models (LLMs). Furthermore, he explores the effectiveness of LLM evaluators in Evaluating the Effectiveness of LLM-Evaluators.

Testing machine learning code and systems is another critical area. Yan’s articles on How to Test Machine Learning Code and Systems and Writing Robust Tests for Data & Machine Learning Pipelines offer practical guidelines for ensuring the reliability and robustness of AI systems. He also cautions against mocking machine learning models in unit tests in his article Don’t Mock Machine Learning Models In Unit Tests.

Synthetic Data, Prompting, and Attention

Generating and using synthetic data for fine-tuning models is a technique that Yan has explored in his article How to Generate and Use Synthetic Data for Finetuning. This approach can be particularly useful for bootstrapping labels via supervision and human-in-the-loop methods, as discussed in Bootstrapping Labels via Supervision & Human-In-The-Loop.

Prompting fundamentals and their effective application are covered in Yan’s article Prompting Fundamentals and How to Apply them Effectively. Additionally, he provides insights into the attention mechanism and the transformer model in Some Intuition on Attention and the Transformer.