Claude-ception: Teaching Claude3 to prompt engineer itself

Summary

In this video, the speaker discusses an innovative workflow for prompt engineering using CLA (Causal Language Model) 3, a tool by Anthropic. The process involves creating a diverse test set, generating responses, evaluating them, and iteratively refining the initial prompt based on feedback. The example given is to mimic the engaging style of Twitter user Elvis in summarizing academic papers. By loading papers reviewed by Elvis, generating tweets with an initial prompt, and then using a feedback loop to improve the prompt, the speaker demonstrates how to create more engaging and stylistically consistent content. The result is a refined prompt that better captures Elvis's tweet style, showcasing the power of iterative prompt engineering with CLA 3.

Takeaways

• 🤖 The script discusses using the language model 'CLA 3 Opus' for prompt engineering, emphasizing its capabilities in generating diverse test sets and iteratively improving prompts based on evaluations.
• 🔄 The workflow involves creating a task and prompt, generating a test set, evaluating generations, and refining the initial prompt using feedback in an iterative process.
• 📚 The example problem tackled is writing style, specifically paper summarization on Twitter, aiming to mimic the engaging style of a user named Elvis.
• 📝 The process starts by loading papers reviewed by Elvis to create a dataset called 'Elvis bot', which serves as the foundation for generating and evaluating tweets.
• 💡 The initial prompt is kept simple and reasonable, focusing on generating tweets that are factual and avoid gimmicks and buzzwords.
• 🔧 Feedback is provided by comparing the generated tweets with example tweets from Elvis, using an annotation queue to refine the prompt based on preferred style.
• 🔄 The feedback loop involves grading the generated tweets, adding better examples as feedback, and regenerating the prompt with the aim of capturing the nuances of Elvis's writing style.
• 📈 The script demonstrates the use of Lang Smith for managing datasets, annotation queues, and prompt regeneration to improve the quality of generated content.
• 🎯 The regenerated prompt incorporates detailed feedback and examples, aiming to produce tweets that are more engaging and stylistically consistent with Elvis's summaries.
• 🚀 The script concludes by showing the improved results of the new prompt on both the test set and a new paper outside the original dataset, highlighting the effectiveness of the iterative feedback process.
• 🌟 The technique is presented as a general approach applicable to various tasks and a promising method for prompt engineering using CLA 3 Opus.

Q & A

What is the main topic discussed in the transcript?

The main topic discussed in the transcript is the process of using the language model CLA (Claw Opus 3) for prompt engineering, specifically to create tweets in a particular writing style, inspired by the Twitter user Elvis.

What is the workflow for prompt engineering with CLA as described in the transcript?

The workflow for prompt engineering with CLA involves starting with a task and a prompt, using Opus to generate a diverse test set, running the prompt against the test set to get a set of generations, evaluating those generations, and using those evaluations to improve the initial prompt iteratively.

What is the specific problem the speaker tries to solve with the workflow?

The speaker tries to solve the problem of writing style, specifically creating paper summaries on Twitter in a style similar to that of the user Elvis, who is known for producing tasteful and engaging paper summaries.

How does the speaker use Langchain and the anthropic API key to load papers for analysis?

The speaker uses Langchain to set up a Lang dataset called 'elvis bot' and loads three papers that Elvis has reviewed from an archive. The anthropic API key is set to utilize CLA three Opus for the analysis.

What is the initial prompt used by the speaker to generate tweets summarizing academic papers?

The initial prompt used is a simple instruction for an assistant to generate tweets to still academic papers, with the guidelines to be all-crafted, avoid gimmicks, and avoid buzzwords.

How does the speaker evaluate the initial generated tweet?

The speaker evaluates the initial generated tweet by comparing it to the style of Elvis's tweets, noting that while it is factual and reasonable, it lacks the 'spiciness' or engaging style that Elvis's summaries have.

What does the speaker do with the generated responses and feedback from Elvis's style?

The speaker uses an annotation queue in Langchain to provide feedback on the generated responses by comparing them to example tweets from Elvis. This feedback is then used to improve the initial prompt.

Can you explain the role of the 'annotation queue' in this process?

The 'annotation queue' is used to provide feedback on the generated tweets. It allows the speaker to compare the generated responses to example tweets from Elvis and input their feedback directly, which is then used to refine the prompt.

What is the 'optimizer prompt' and how is it used?

The 'optimizer prompt' is a new prompt created by the speaker to take into account the feedback provided in the annotation queue. It is used to regenerate a new prompt that better captures the style of Elvis's tweets.

How does the speaker test the new prompt?

The speaker tests the new prompt by running it against their evaluation set (the three papers initially loaded) and adding the new generations to the annotation queue for further review and feedback.

What is the final outcome of the process described in the transcript?

The final outcome is a new prompt that has been optimized to generate tweets in a style similar to Elvis's paper summaries, making them more engaging and adhering to the desired writing style.