Cognitive Prompting(인지적 프롬프팅) 공유회 full ver.

Summary

The video script delves into the innovative concept of 'Cognitive Prompting', a technique designed by Changju Kim to enhance the utility of GPT models in extracting more valuable information. Introduced on November 8 through a Facebook post by Changju, this approach applies interviewing techniques to prompts, aiming to draw deeper insights. The speaker shares their insights from attending Changju's lecture, emphasizing the importance of community learning, practice, and feedback. The discussion covers various strategies for prompting, comparing cognitive prompts with standard engineering prompts, and showcases the practical application of these techniques in different scenarios. The script concludes with a challenge to the audience to apply these techniques and share their findings, fostering a collaborative learning environment.

Takeaways

• 📚 Cognitive Prompting, abbreviated as 'Cog Prompt,' is a technique designed by Changju Kim for enhancing information retrieval from language models like GPT by applying interview techniques commonly used in human interactions.
• 🔍 The concept originated from a Facebook post by Changju Kim on November 8, initiating the idea that cognitive prompts could draw deeper insights from language models.
• 💻 Cognitive Prompting differs from traditional prompt engineering by incorporating a more cognitive, psychological approach, aiming to extract more in-depth information.
• 🧐 The course on Cognitive Prompting emphasizes community learning, where students practice, give feedback, and share discoveries, highlighting the importance of communal learning in mastering the technique.
• 🚀 Techniques include turning a junior AI into an expert by asking it to approach queries as an expert would, significantly enhancing the quality of responses.
• 📈 Low-level and high-level requirements in Cognitive Prompting allow for asking questions at a higher abstraction level, especially useful when one's knowledge in the field is limited.
• 📲 Utilization of GPT as a 'consultant' for in-depth, cognitive prompts demonstrates its capability to serve as a more sophisticated tool for inquiry and problem-solving.
• ✨ Specific strategies like 'Chain of Thought' (COT) and asking for step-by-step problem solving improve the model's performance on complex tasks by breaking them down into simpler steps.
• 💡 Innovations in prompt crafting, such as incorporating variance and opposition to stimulate more diverse and accurate responses from the model, showcase advanced prompt engineering techniques.
• 🌟 The presenter encourages active experimentation with different prompting techniques, emphasizing the importance of personal exploration and adaptation of Cognitive Prompting methods.

Q & A

What is Cognitive Prompting?

Cognitive Prompting refers to techniques used to extract more useful information from language models like GPT by applying interview techniques commonly used in human interactions to prompts.

Who devised the concept of Cognitive Prompting?

The concept of Cognitive Prompting was devised by Changju Kim.

How does Cognitive Prompting differ from traditional prompt engineering?

Unlike traditional prompt engineering, which is more technical, Cognitive Prompting involves applying psychological and cognitive approaches to elicit deeper and more insightful responses from language models.

Can attending Changju Kim's course be substituted by just learning about Cognitive Prompting?

No, attending Changju Kim's course cannot be substituted by just learning about Cognitive Prompting since the course offers comprehensive learning through classes, community interaction, practice, and feedback, which are crucial for mastering the technique.

What are some benefits of using Cognitive Prompting techniques?

Benefits include obtaining more in-depth responses from language models, enhancing the quality of interaction by approaching prompts with a more expert interview style, and being able to ask higher-level questions even when one has limited knowledge on a subject.

How does the practice of Cognitive Prompting improve interactions with GPT models?

By applying Cognitive Prompting, users can mimic expert-level inquiries, leading to more accurate and comprehensive responses from GPT models, and it enables asking complex questions more effectively.

What role does community and feedback play in learning Cognitive Prompting according to the script?

Community and feedback play a crucial role in learning Cognitive Prompting as they provide opportunities for practice, sharing insights, and receiving constructive feedback, which are essential for improving one's prompting skills.

Why is it important to consider both low-level and high-level requirements when using Cognitive Prompting?

Considering both low-level and high-level requirements allows for more tailored and precise questions, enabling users to extract relevant information from GPT models more effectively, especially in areas where the user's knowledge is limited.

How can Cognitive Prompting techniques be personalized or adjusted based on individual learning from Changju Kim's course?

Individuals can personalize Cognitive Prompting techniques by integrating their own research, adjusting existing methods based on personal insights, and applying feedback from community practice to refine their approach.

What is the significance of practice and real-world application in mastering Cognitive Prompting?

Practice and real-world application are significant in mastering Cognitive Prompting as they help solidify the understanding of concepts, allow for experimentation with different prompting strategies, and enable learners to see the practical impact of their techniques.