Title

AWS re:Invent 2023 - Accelerate experiment design with Amazon Bedrock (DOP217)

Summary

• Robert Neal from LaunchDarkly's Decision Science group discussed the integration of generative AI, specifically Large Language Models (LLMs), into experimentation systems.
• LaunchDarkly, originally a feature flagging company, has evolved to support a wide range of industries with its experimentation products.
• Neal emphasized the importance of decoupling deploys from releases and the critical role of experimentation in product development.
• He highlighted the common pitfalls of relying on gut feelings for product decisions and the superiority of experimentation over pre-post measurement.
• The talk focused on the benefits of parameterizing code and using feature management systems to optimize business value without frequent code changes.
• Neal introduced the concept of using generative AI to explore the parameter space more efficiently, allowing for better experimentation and personalization.
• He demonstrated how LaunchDarkly's AI assistant can suggest experiment types, metrics, and feature flag variations based on the user's hypothesis and historical data.
• The integration with Amazon Bedrock and the Titan model was showcased, highlighting the ease of incorporating generative AI into product development.
• Neal concluded by discussing the transformative potential of generative AI in product design and enhancement, and the use of LaunchDarkly for building generative AI products.

Insights

• Generative AI can significantly improve the experimentation process by suggesting a wide range of variations and optimizing the exploration of the parameter space.
• The integration of generative AI into LaunchDarkly's product allows users to generate more ideas and variations without increasing complexity or requiring domain expertise.
• Amazon Bedrock's Titan model has proven to be a valuable tool for LaunchDarkly, enabling rapid validation and integration of generative AI features.
• The talk highlighted the importance of pre-registration in experimentation to avoid the pitfalls of the replication crisis, ensuring that hypotheses are stated before experiments are run.
• LaunchDarkly's approach to building generative AI features is threefold: rethinking products entirely with generative AI, building new features with a generative AI-first approach, and enhancing existing features with generative AI.
• The company also provides guidance on using LaunchDarkly for developing generative AI products, emphasizing the platform's utility in fine-tuning and experimenting with generative AI pipelines.