How Microsoft ML Researcher Actually Uses ChatGPT | Summary and Q&A

February 14, 2024
The AI Advantage
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How Microsoft ML Researcher Actually Uses ChatGPT


Discussion between a power user and a Microsoft researcher on prompt engineering, code generation, and debugging using Chat GPT.

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Key Insights

  • 🦮 Prompt engineering involves defining instructions and context to guide the model's response.
  • 😫 Custom instructions and preferences can be set in advance to tailor the model's behavior.
  • 💭 Multi-shot prompting can be combined with chain of thought reasoning for advanced code generation.
  • 🦻 The model's ability to provide insights and aid in debugging is valuable for developers.


all right so in today's video I'll be talking to a principal researcher at Microsoft and I thought this conversation was quite interesting because we bring such different viewpoints to it as you know I always come from the perspective of power user a tech enthusiastic consumer that obsesses over finding what's possible with these tools and she come... Read More

Questions & Answers

Q: What are the basic components of prompt engineering?

Prompt engineering involves two parts - instructions and context. Instructions define the task, while context narrows down the possibilities and can include code base or other relevant information.

Q: How can multi-shot prompting be integrated into advanced code generation?

Multi-shot prompting can be useful in providing similar problems or pseudo code solutions to guide the model's code generation. It can also be used alongside chain of thought reasoning to help the model focus on important parts of the solution.

Q: Do you use custom instructions in the web interface?

The researcher uses custom instructions, specifying different setups for general information and code-related tasks to enhance productivity and maintain structure.

Q: How can Chat GPT be used for debugging?

Chat GPT can be used for debugging by isolating specific functions or code blocks and asking the model to identify potential errors or provide insights into possible issues.

Summary & Key Takeaways

  • The conversation focuses on prompt engineering and enhancing prompts with techniques like following up and multi-shot prompting.

  • Custom instructions and their use in the web interface are discussed, along with the benefits of specifying preferences in advance.

  • The researchers discuss the challenges and techniques for using large language models for code generation and debugging.

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