How to Setup LLM Evaluations Easily (Tutorial)

Introduction to Model Evaluations 00:00

• Emphasizes the importance of measurements in improving AI models, specifically focusing on RAG (Retrieval-Augmented Generation) evaluations.
• Highlights the necessity for accurate information in business applications, such as chatbots, to avoid potential issues.
• Introduces Amazon Bedrock as the platform for conducting model evaluations, featuring a range of models from various providers.

Setting Up AWS for Evaluations 01:12

• Walks through creating an AWS account and setting up an IAM user for executing evaluations.
• Details the process of creating a user group with appropriate permissions for evaluation tasks.

Preparing Knowledge Base and Prompts 04:21

• Discusses the need to upload a hotel policy document to an S3 bucket for use in the chatbot application.
• Guides on creating additional buckets for prompts and evaluation storage, including setting permissions for cross-origin resource sharing (CORS).

Creating Knowledge Bases 08:00

• Explains the steps to create a knowledge base using a vector store and the importance of syncing it for evaluations.
• Describes the selection of an embeddings model (Amazon's Titan text embeddings v2) for processing the knowledge base.

Conducting Evaluations 09:50

• Demonstrates how to create RAG evaluations within Amazon Bedrock, selecting an evaluator model and defining metrics for benchmarking.
• Highlights the option to use custom metrics tailored to specific requirements.

Reviewing Evaluation Results 14:01

• Shows how to access and interpret evaluation results, including the distribution of helpfulness and correctness scores.
• Provides insights into individual evaluation outputs, referencing the generation output against ground truth responses.

Comparing Evaluation Metrics 16:25

• Discusses the ability to compare evaluations from different models, showcasing the performance metrics of Nova Pro versus Nova Premiere.
• Highlights the importance of continuous benchmarking to improve AI model performance.

Conclusion and Resources 17:29

• Thanks Amazon for their partnership and encourages viewers to explore provided resources and sample data for further learning.