OpenAI CEO's Testimony + Prompt Engineering Tips + AI News From This Week

Using AI

18-05-2023 • 53 mins

The implications of Sam Altman's testimony plus prompt hacking / prompt engineering tips. EU AI Act, Open Source OpenAI Models and much more.


Watch this on Youtube

AI News this week Sam Altman’s abridged testimony (Twitter):

Senate sub-comittee hearing in full:

Sam Altman Testimony on TikTok

Genie AI Linkedin


Sparks of AGI Microsoft Paper

OpenAI open source model


Crazy things this week - Effortless meal planning with AI-generated recipes

Elon saying OpenAI wouldn’t exist without him.

Discussion Points

  • AI generated opening statement by Richard Blumenthal
  • Sam Altman (OpenAI), Gary Marcus (Uber,, Christina Montgomery (IBM)
  • Senate is aware they don’t understand this enough to legislate on it
  • OpenAI is making significant efforts towards safety
  • OpenAI advocates for a committee to regulate AI development for large companies
  • Election fraud seems to be a major concern for OpenAI
  • New, better jobs will be created according to OpenAI

Prompt hacking tips

  1. Be succint and specific!

  2. Prime the language model by providing a persona for it.

  3. Asking the model to think through its response step-by-step “Let’s think step-by-step”

  4. Providing clear context: Offer concise background information for guidance. Use Cases: Definition explanations, historical event summaries, concept descriptions.

  5. Specifying the output format: Indicate desired answer structure explicitly. Use Cases: Generating lists, step-by-step instructions, summarizing long texts.

  6. Using explicit instructions: Request specific detail or critical thinking. Use Cases: Debating pros and cons, analyzing biases, evaluating arguments.

  7. Redundancy and rephrasing: Reinforce information by reiterating questions. Use Cases: Clarifying ambiguous topics, extracting specific details, verifying facts.

  8. Temperature and token settings: Adjust randomness and output length. Use Cases: Creative writing, focused summaries, generating multiple response variations.

  9. Iterative refinement: Refine prompts based on previous responses. Use Cases: Troubleshooting, problem-solving, narrowing down complex topics.

  10. Prompt engineering: Craft effective prompts using templates or examples. Use Cases: Analogies, translating complex topics into simple explanations, generating structured responses.

  11. Advanced techniques like asking the language model to think step by step

Shout out to DeepLearning.AI’s free prompt engineering course:

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