prompting

Chain-of-Vibes by Pete Hodgson sums up nicely the approach I take to leverage the power of agentic AI workflows while sidestepping the limitations. Treat the tools “like weirdly knowledgeable, hyper-productive junior engineers”, give enough context but otherwise limit their output. Importantly, review the outcome of each small task and make a decision:

A) Accept and commit
B) Prompt to adjust
C) Manually fix up
D) Revert and re-prompt

The last item is an important one that’s often overlooked - throwing away the result and fixing your prompt is often more time efficient, especially when learning how to prompt.

Prompting has deceptive simplicity. It seems like it should be straight forward but for me it’s required a lot of practice to get better results. Aidan Boyd’s post How I talk to AI has a good summary of how he thinks about prompting before doing so.

Fuck you, show me the prompt is an investigation into extracting the actual prompt that is sent to a model by llm abstraction libraries.

There are many libraries that aim to make the output of your LLMs better by re-writing or constructing the prompt for you. The prompts sent by these tools to the LLM is a natural language description of what these tools are doing, and is the fastest way to understand how they work.

Prompting Fundamentals and How to Apply them Effectively has some really good prompting guidance.

Marc Andreessen on navigating a model’s latent space via prompting.