How to Setup LLM Evaluations Easily (Tutorial)
AI Summary
This video tutorial demonstrates how to perform sophisticated Model and Retrieval-Augmented Generation (RAG) evaluations using Amazon Bedrock. It explains the importance of evaluating AI models, especially chatbots interfacing with customers, to ensure accurate and reliable information.
The presenter uses the example of a hotel policy document (26 pages) as the knowledge base to power a hotel chatbot answering user queries about terms of service and policies. The video guides through setting up an AWS account, creating IAM users and permission groups, and configuring S3 buckets to store the knowledge base, evaluation prompts, and results.
Next, it shows how to create a vector store knowledge base using Amazon’s Titan text embeddings, sync it, run rag evaluations with various models (including Amazon’s new large context model Nova Premiere 1.0), and select evaluation metrics like helpfulness, correctness, faithfulness, coherence, and responsible AI criteria.
Evaluation results and metrics visualization are covered, including detailed example questions and model-generated answers compared to ground truth. The video concludes by demonstrating how comparison between multiple model evaluations can guide improvements in AI model choice and performance.
The tutorial emphasizes the critical role of systematic benchmarking for scaling AI implementations in production environments and highlights that all supporting files and scripts are provided in the description for viewers to follow along.