Enhancing AI Output
Linus and Dan discuss strategies for improving the output of language models, such as ChatGPT. They explore the idea of breaking down the process into multiple steps and prompting the AI to think through each step, resulting in more concise and accurate outputs.In this clip
From this podcast
AI & I
Inside the Mind of an AI Researcher: ChatGPT and Notion AI Experiments - Ep. 3 with Linus Lee
Related Questions
How are prompts used in AI models as discussed in the episode Supercharging Developer Productivity with ChatGPT and Claude with Simon Willison - 701 and the clip Crafting Effective Prompts?
Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 and the clip LLMs as Jury, as well as in the episode Collaboration & evaluation for LLM apps and the clip Fine Tuning Insights?