The tutorial demonstrates how to set up and run retrieval-augmented generation (RAG) evaluations using Amazon Bedrock to ensure AI chatbots provide accurate responses based on a hotel policy document. It guides viewers through configuring AWS infrastructure, creating a knowledge base, running evaluation jobs with custom metrics, and analyzing results to benchmark and improve model performance.
The video tutorial focuses on the importance of model evaluations, specifically retrieval-augmented generation (RAG) evaluations, to ensure AI chatbots provide accurate and reliable information. Using Amazon Bedrock, a fully managed service offering access to top AI models from providers like Amazon, Meta, and Anthropic, the presenter demonstrates how to set up and run sophisticated model evaluations. The use case involves creating a chatbot for a hotel that can answer complex questions based on a 26-page hotel policy document, emphasizing the need for accurate responses to avoid customer confusion or issues.
The tutorial begins with setting up the necessary AWS infrastructure, including creating IAM users with appropriate permissions and configuring three Amazon S3 buckets. These buckets store the hotel policy document (knowledge base), the prompts with example questions and ground truth answers (test set), and the evaluation results. The presenter walks through uploading the policy document and prompts to their respective buckets and configuring Cross-Origin Resource Sharing (CORS) permissions to allow access by other services.
Next, the video covers creating a knowledge base in Amazon Bedrock using the uploaded hotel policy document. This involves converting the document into a vector store using Amazon’s Titan text embeddings model, enabling efficient querying by the language model. The knowledge base must be synced to prepare it for evaluation. The presenter also demonstrates testing the knowledge base directly to ensure it responds correctly to queries before proceeding to evaluations.
The core of the tutorial is setting up the RAG evaluation job in Amazon Bedrock. The presenter selects an evaluator model (Sonnet 3.7v1) and configures the evaluation to test both retrieval and response generation capabilities of the chatbot. They choose the Nova Premiere 1.0 model for inference and select multiple evaluation metrics such as helpfulness, correctness, and faithfulness. The evaluation uses the prompts stored in S3 and outputs results to the designated evaluation bucket. The video highlights the flexibility to add custom metrics and use external models or data sources if desired.
Finally, the presenter reviews the evaluation results, showing detailed scoring and explanations for individual prompts, including how well the model’s responses align with the ground truth. They demonstrate comparing different models’ performance side-by-side to identify which performs better on specific metrics. The tutorial concludes by emphasizing the critical role of evaluations in scaling AI applications and encourages viewers to use these tools to benchmark and improve their models continuously. All resources, including sample data and configuration settings, are provided in the video description for easy replication.