Success (and failure) in prompting
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.
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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!
(00:00 ) - Welcome to Practical AI
(00:43 ) - Best time for an AI podcast?
(05:19 ) - What makes output good or bad?
(15:36 ) - Sponsor: Changelog++
(16:36 ) - Behind the behavior
(19:23 ) - What can we reliably expect?
(29:14 ) - Prompt Engineering
(35:11 ) - Tips on engineering prompts
(43:05 ) - Outro