Dive into a comprehensive 51-minute podcast episode featuring Shahul Es, a data science expert and Kaggle Grandmaster, as he explores the intricacies of evaluating Large Language Model (LLM) applications. Learn about debugging techniques, troubleshooting strategies, and the challenges associated with benchmarks in open-source models. Gain valuable insights on custom data distributions, the significance of fine-tuning in improving model performance, and the Ragas Project. Discover the importance of evaluation metrics, the impact of gamed leaderboards, and strategies for recommending effective evaluation processes. Explore topics such as prompt injection, alignment, and the concept of "garbage in, garbage out" in LLM applications. Connect with the MLOps community through various channels and access additional resources, including job boards and merchandise.
Evaluating LLM Applications - Insights from Shahul Es
Overview
Syllabus
[] Shahul's preferred coffee
[] Takeaways
[] Please like, share, and subscribe to our MLOps channels!
[] Shahul's definition of Evaluation
[] Evaluation metrics and Benchmarks
[] Gamed leaderboards
[] Best at summarizing long text open-source models
[] Benchmarks
[] Recommending evaluation process
[] LLMs for other LLMs
[] Debugging failed evaluation models
[] Prompt injection
[] Alignment
[] Open Assist
[] Garbage in, garbage out
[] Ragas
[] Valuable use case besides Open AI
[] Fine-tuning LLMs
[] Connect with Shahul if you need help with Ragas @Shahules786 on Twitter
[] Wrap up
Taught by
MLOps.community
Related Courses
-
Improving Accuracy of LLM Applications
-
LLM Evaluation: Challenges and Best Practices - MLOps Podcast #210
-
Navigating the AI Frontier: The Power of Synthetic Data and Agent Evaluations in LLM Development
-
Enterprises Using MLOps - The Changing LLM Landscape and MLOps Pipelines
-
Retrieval Augmented Generation - Techniques and Applications
-
Designing for Forward Compatibility in Gen AI - MLOps Podcast
