About this episode
With the recent proliferation of generative AI models (from OpenAI, co:here, Anthropic, etc.), practitioners are racing to come up with best practices around prompting, grounding, and control of outputs. Chris and Daniel take a deep dive into the kinds of behavior we are seeing with this latest wave of models (both good and bad) and what leads to that behavior. They also dig into some prompting and integration tips. Join the discussion Changelog++ members save 2 minutes on this episode because they made the ads disappear. Join today! Sponsors: Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com Fly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs . Changelog++ – You love our content and you want to take it to the next level by showing your support. We’ll take you closer to the metal with extended episodes, make the ads disappear, and increment your audio quality with higher bitrate mp3s. Let’s do this ! Featuring: Chris Benson – Website , GitHub , LinkedIn , X Daniel Whitenack – Website , GitHub , X Show Notes: Generative AI model behavior in the news: Microsoft’s AI chatbot is going off the rails A Conversation With Bing’s Chatbot Left Me Deeply Unsettled Sydney’s gaslighting ChatGPT political bias Stable Diffusion amplification of stereotypes Useful guides related to prompt engineering: co:here prompt engineering guide Prompt engineering overview from Elvis Savaria 10 Amazing Resources For Prompt Engineering, ChatGPT, and GPT-3 Image generation prompt engineering guides: see here and here Something missing or broken? PRs welcome!