We're so happy to invite you to our first-ever all-day event! Welcome to the inaugural GenAI Days Paris, a single track conference, that's all about pushing boundaries and exploring new horizons. Talks from innovators in the space at companies such as GitHub, Meta, Theodo, Carrefour and more.. Join us on 30th November in Paris for the first day of its kind.

The rise in the use of Large Language Models (LLMs) has highlighted the challenges of running them in production environments. In response, AWS has unveiled Bedrock, a platform-centric, model-agnostic, and serverless solution, addressing the adoption needs of Generative AI. As a serverless service, Bedrock eliminates the infrastructure management associated with running LLMs at scale and simplifies pricing structure for businesses with a familiar pay-per-use model. Amazon Bedrock offers features like easy model selection, autonomous agents, and fine-tuning, fostering a rich ecosystem for developers. While it shares similarities with OpenAI's platform, Bedrock's broader model support, clearer data governance policies, and competitive pricing make it a compelling choice. In essence, Amazon Bedrock is not merely an AWS service but a foundation for integrating GenAI into applications, signifying the pivotal role GenAI will have in future products and services.

The emergence of Generative AI (GenAI) applications, powered by Large Language Models (LLMs), has introduced a new approach to leveraging AI for creative content generation, code writing, and problem-solving. However, ensuring the quality and consistency of GenAI applications presents unique challenges due to the non-deterministic nature of LLMs. In this article, we explore the testing challenges faced while developing a GPT application (Code Review GPT) and propose a test pipeline to address these complexities. The pipeline involves defining test cases, generating test prompts from plain text descriptions, and evaluating LLM responses using snapshot comparisons based on document embeddings. By adopting a similar testing approach, developers can confidently update prompts, maintain application stability, and deploy GenAI applications with enhanced reliability.

GenerativeAI (GenAI) has the potential to revolutionize many aspects of our lives, but it also presents significant risks that must be carefully managed. These risks include IP and data security concerns, the potential for malicious use, and implicit bias. It's essential to understand and mitigate these risks throughout the entire supply chain of GenAI applications to avoid legal, societal, and reputational risks. Transparency and accountability across the entire supply chain are key focuses in achieving this goal.