Transitioning a generative AI (genAI) project from a simple Proof of Concept (PoC) or service demo to a robust, production-ready solution is a complex process fraught with numerous challenges. This talk aims to guide attendees through the intricacies of scaling genAI projects, highlighting common pitfalls and strategic solutions.

In this talk, we introduce a comprehensive architectural blueprint for genAI projects, covering essential aspects such as prompt and guardrail versioning, conversation management, vector database selection, and model evaluation, utilizing Amazon Bedrock’s Evaluation and Guardrails.

A highlight of this talk is a case study demonstrating the implementation of an End-to-End Retrieval-Augmented Generation (RAG) system integrated with Bedrock Knowledge with prompt versioning, knowledge-optimized chunking, conversation history, and knowledge base augmentation through agents as well as vector database and models selection.