Prompt Engineering

Summary

Chain-of-thought is powerful, but which chain? Short explanations work for easy tasks, long reflections help on hard ones, and code sometimes beats them both. What if your model could adaptively pick the best strategy, per task, and improve as it learns?

The Adaptive Reasoning Model (ARM) is a framework for teaching language models how to choose the right reasoning format direct answers, chain-of-thoughts, or code depending on the task. It works by evaluating responses, scoring them based on rarity, conciseness, and difficulty alignment, and then updating model behavior over time.

“If you’ve ever pasted 50 prompts into an image generator one-by-one, this is for you. I hit my limit and built Clipper to solve it.”

📖 Summary

In the previous blog post I wrote a research paper: Cross-Modal Cognitive Mapping. This paper is about turning your conversations into images to gradually map your thought patterns. The implementation of this paper is an application called Prism.

A component of this app is image generation from prompts or your conversations. All of the Foundation models support this but it’s a pretty janky process where you have to generate the prompt paste it into a text box and download the image. I just went through a week of doing this while building a prompt toolkit. While I was doing this I kept wishing I built the app which I’m going to share with you now.

Summary

This post provides a comprehensive guide to prompt engineering, the art of crafting effective inputs for Large Language Models (LLMs). Mastering prompt engineering is crucial for maximizing the potential of LLMs and achieving desired results.

Effective prompting is the easiest way to enhance your experience with Large Language Models (LLMs).

The prompts we make are our interface to LLMs. This is how we communicate with them. This is why it is important to understand how to do it well.