Safety in LLMs Using Chain-of-Thought: Lessons From Constitutional AI
In this webinar, we will better understand how Anthropic used chain-of-thought prompting to improve the quality of LLM responses, reducing harm and improving safety. After the LLM responds, send additional prompts to measure how the LLM response differs from the intended rules (or constitution) to generate a higher-quality response. While this technique is built into Anthropic's Claude LLMs to reduce harm and toxicity, it can be applied to any LLM model for a number of rules. We'll explore (1) additional performance improvements achieved by chain-of-thought prompting; and (2) how you can establish your own constitution to both measure and improve the safety and quality of your LLM application.
You'll learn the following in this webinar:
Understanding chain-of-thought prompting
Comparison of RLHF (human feedback) and RLAIF (AI feedback) preference LLM models in practice
Use cases and best practices for chain-of-thought prompting to improve LLM response quality and safety
About WhyLabs:
WhyLabs, Inc. (www.whylabs.ai / @whylabs) enables teams to harness the power of AI with precision and control. From Fortune 100 companies to AI-first startups, teams have adopted WhyLabs’ tools to secure and monitor real-time predictive and generative AI applications. WhyLabs’ open source tools and SaaS observability platform surface bad actors, bias, hallucinations, performance decay, data drift, and data quality issues. With WhyLabs, teams reduce manual operations by over 80% and cut down time-to-resolution of AI incidents by 20x.
Learn more about WhyLabs and our open source projects:
LangKit: An open-source toolkit for monitoring LLMs
whylogs: the open source standard for data logging