Hi, my name is Devin.

My expertise with AI lies in the design and architecture of modern generative AI solutions. By harnessing the power of SPRs and cutting-edge techniques, I create solutions that offer personalized assistance and precise simulations of the action space, transforming static workflows into dynamic, evolving processes.

• Advanced knowledge management and workflow design
• Knowledge architectures using semantic graphs
• Predictive modeling using novel fine-tuning methods to create customized solutions for any domain
• Conversational solutions enabling accessible interactions with complex models

• Explainable generative reasoning using composable modules
• Multimodal knowledge integration through hybrid techniques
• High-dimensional architectures for unique challenges
• Focus on model functionality over physical resemblance

Sparse Priming Representations (SPRs) are a technique used in advanced Natural Language Processing (NLP) and particularly with Large Language Models (LLMs).

Key Principles

• Distilling Complex Ideas: SPRs aim to represent complex ideas, memories, or concepts using a concise set of keywords, phrases, or statements. This mimics the way human memory compresses information for efficient storage and recall.
• Targeting the Latent Space: LLMs possess a vast "latent space" of embedded knowledge and abilities. SPRs act as refined cues that activate specific, relevant regions within this latent space, leading to more efficient and focused responses from the LLM.
• Flexibility and Versatility: SPRs aren't rigid; they can manifest in various ways, including prompts, workflows, knowledge representations and more. This adaptability is an inherent strength of the SPR concept.

Why SPRs Matter

1. Efficiency and Precision: SPRs streamline interactions with LLMs, making them computationally less expensive and enabling faster processing of information.
2. Enhanced Task Performance: The "sparse" nature of SPRs leads to more relevant and accurate responses for complex tasks.
3. In-Context Learning: SPRs can drive dynamic in-context learning within LLMs, allowing the model to improve its comprehension and execution based on concise information provided.

• The library is hosted on multiple databases in a Notion library. Due to the complexity and depth of the content, this is the most manageable way to present this information. In the following weeks I will convert to a webpage.
• Each page has multiple forms of that solution presented. This could be workflows, prompts, frameworks, or templates.
• Everything presented here is a working solution, however these are not the final, refined forms. To realize these solutions for yourself, you will need to adapt them and experiment.
• Sometimes things are “simulated”, that is irrelevant as long as it functions as intended.
• You will notice many of the complex workflows have an end section that sounds like an initial response from the AI. That is very intentional. Ideally you can include this section in your initial prompt or GPT. Because this is an excellent way to “skip” the need for an initial step, straight to execution. Additionally, it helps frame the workflow in a manner that clearly shapes how the AI should be interacting with the presented logic.

I am publicly releasing these advanced SPR applications for three main reasons:

• Knowledge Sharing: You will not find a better resource for SPR usage around.
• Prompt Engineering Empowerment: Prompt engineers can dramatically streamline and fine-tune their work using the techniques and structures I share here.
• Innovation Catalyst: This knowledge should inspire entirely new ideas and directions in prompt engineering and LLM interaction.

I will run a bonus with this release: If you are to fully realize a solution presented here in a zero-shot setting, that works for varied user inputs as a self-contained solution over multiple steps. I will shoutout and feature your GPT/solution on my discord.

AI Template & SPR Library

Please join the discord or email me at [email protected].