Automatically creating new content from old

You know what I hate? Repetition. Still, a difficult lesson that I’ve leaned, through both experience and discussions with experts in marketing, is that repetition is the key to getting one’s message across. I like to believe that a single, coherent and cogent conveyance of thought is all it takes to deliver, for example, a new security model or change in policy, or whatever. In reality, the propagator of the message –in this case, the security team– has to deliver the message, announce it a second time, send an email, post a document on the intranet, have more meetings with the same people, make sure to cover the topic on various 1:1 meetings, etc. etc. ad infinitum.

And that is why I have been working with Daniel Miessler’s fabric framework to devise a prompt that can take variations of the same messaging and create new, yet fresh, versions of the same message. So far, the following prompt definition has been pretty strong in creating new content from old ideas.

My typical approach is to run the same prompt though multiple #LLM models

cat examples.md | fabric --model gemma2:latest -sp create_similar

Interestingly, in a couple test runs with different example files, I found that #OpenAI’s models under-performed the open source models I used via #ollama. To be clear, this is uncommon. Most of the #fabric prompts appear to be tuned for –or at least work better with– OpenAI models. With this particular prompt, this was not the case. No matter; my approach to using genAI and LLMs is to run a prompt and content through multiple inference servers, models, and even different temperatures in order to create a collection of data that I can then hand-mold into something usable[^1].

Fabric Prompt

Here is the system.md contents from the fabric “create_similar” prompt

# INPUT

INPUT:

# IDENTITY and PURPOSE

You are an advanced analytical AI adept at extracting specific points from many examples, identifying similarities and differences, synthesizing what works best, and then creating a similar but much much better version of the example.  The input contains a number of examples of the type of content needing to be synthesized.  The first section titled "Purpose" describes the nature of the examples and indidcates the topic and information to be conveyed.  Documents will be delineated with a title such as 'EXAMPLE 1' specifically calling out the beginning of a new document.  The end of each example is delineated with a footer such as 'EXAMPLE 1 END'.  Your objective is to understand the style and format of the document examples and create new similar content.

# STEPS

1. Review each document carefully, taking the time to extract and understand the primary points made in each one.
2. Compare and contrast the list of points from each document against the points made in the other documents
3. Extract the key points made by the examples, taking particular note of similarities between them.
4. Output the results as specified in the output instructions.


# OUTPUT INSTRUCTIONS

Craft and create a new document using the format and style identified from the examples.  The output must be new and novel while keeping to the themes and style of the examples.  Do not use any language from the examples verbatim.

I have found that local models, such as gemma and llama3 work a bit better by putting the INPUT field

Purpose

Up top, I mentioned that it has taken me some time to learn that repetition is the requirement for conveying a message, and a longer time to put this into regular practice. This goes 3x for our individual contributor team members. Personally, I don’t like this because it feels inefficient, but ICs don’t do it because they are very busy, and re-visiting something takes away from closing out the next item in their queue. Nonetheless, repetition is paramount, so devising low-interaction methods to revisit is crucial for regular operations.

Footnotes

[^1]: I have a feeling that I could do most of the work faster if I simply did it by hand. However, running through multiple LLMs in this manner provides a bit of randomness, and helps me remain apprised on the state-of-the-art for this strange field of study.