Using GPT4 to first construct a complex prompt for handwriting analysis, then using GPT-4V to execute the prompt with the handwriting samples. Truly fascinating example of what you can do with this model, and the high degree of accuracy and detail you can produce with a complex use case.

Every person in the Air Force has a series of interlinked personnel based activity reports that they are either required or optionally available to them relative to performance. These reports have an interlinked position in time and scope, each building from the previous, and supporting the next in the chain. A simple example would be personnel performance. At the end of the chain you could have a medal package for the award in the recognition of achievement, and you could follow that record of action that justified the achievement back through a series of documents each decreasing in scope and time, all the way down to the smallest and shortest form for weekly activity tracking.

With so many people making the pilgrimage to the DC area for AFA this week, I thought this would be a timely topic to bring up. Remember that LLMs fundamentally rely on mass amounts of data to function well. As good as scraping the entirety of the internet is, I think we can all agree that’s neither authoritative nor necessarily as deep or accurate as any topic actually goes. So where are the most detailed and accurate sources of knowledge on any particular domain? In the minds of the people that work in those domains everyday.

Unlike my four previous posts on the topic of our AI study on LLM technology, this one is more a collage of items that didn’t really fit well enough to include in the written presentation. They might come up in a verbal presentation, but not likely unless a question drove the interest in a deep dive. But I like having some sort of written record beyond just my endless pages of random notes, so I thought this might make an interesting post to share.

Continuing on in the series of lessons learned from the chatbot study, let’s look now into the art of prompt engineering.

Continuing on in the series of lessons learned from the chatbot study, next let’s discuss use case discovery.

Continuing on in the series of lessons learned from the chatbot study, we find a clear but important distinction.

It is time to conclude the AI study focusing on LLM-based Chatbots I have been leading as my current MPA orders conclude with AETC. As I have no expectation that I will get approval for a full public release of the study results in either PowerPoint or Research Paper formats, I wanted to make a series of posts focusing on the major thematic lessons learned instead. All of these are completely unclassified, and won’t include any sensitive use case data or direct descriptions. Ultimately though the real value is in the knowledge gained not the specific assessments conducted.

Made famous by the remarks of Donald Rumsfeld in 2002, the quad chart of below can provide valuable insight to the application of LLMs within any domain. There are two continual issues with LLM utilization that both functionally and technically are unavoidable.

Here is a description on the process and key documents required for personnel at the squadron level in the Air Force to request and gain approval for new funding of $2 million or more:

Plenty of companies out there pitching custom trained Large Language Models (LLMs), and/or their services to custom train LLMs for the Air Force on our data. To save everyone’s time here’s the absolutely free and publicly accessible crown jewels of the Air Force’s information kingdom. Let the best model win!

The US Department of Defense (DoD) is at a crucial point in its initial planning stages for how best to deploy capital to develop and purchase AI systems for a range of functions across the services. The plan offered below gives some insight to how the services could leverage the strengths of diverse contracting tools to foster innovation and maintain the DoD's competitive edge. The following plan outlines a suggested order of contract execution and potential parallel implementation of these contract types, to provide both high effectiveness and efficiency of their combined outputs.

The US Department of Defense (DoD) employs various FAR (Federal Acquisition Regulation) based and Non-FAR based contract types to foster military innovation and maintain a competitive edge. This article delves into the advantages, best use cases, and potential disadvantages of four such contract types: Other Transaction Authority (OTA), Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs, Cooperative Research and Development Agreements (CRADAs), Prize Competitions, Partnership Intermediary Agreement (PIA), and Technology Investment Agreement (TIA).

Though I know this might be upsetting to some. The chart above shows some general terms of how the decision and action matrix works for acquisition decisions across a range of variables, but this is mainly depicting cost as the central organizing metric.

Broad Agency Announcements (BAAs) are a unique acquisition tool used by the United States government to solicit proposals for research and development projects. These announcements allow the government to engage with academia, industry, and other organizations to drive scientific and technical innovation in areas of strategic interest. This essay will delve into the history of BAAs, compare their advantages and disadvantages to other acquisition tools, and present best practices for both government agencies and proposers.

The Rapid Acquisition Authority (RAA) is a crucial instrument for the United States government to expedite the procurement of research and development (R&D) projects, enabling them to respond quickly to emerging needs and maintain a technological edge in various fields. This essay will examine the history of the RAA, compare its advantages and disadvantages to other contract types, and outline best practices for implementing it effectively.

Military Innovation Professionals have to be skilled in three interrelated areas of Acquisition: Program Management, Funding, and Contracting. Let's play some Innovation Moneyball with tricks of the trade.

Other Transaction Authority (OTA) contracts have emerged as an innovative mechanism for funding research and development (R&D) projects in the government sector. These contracts have provided agencies with increased flexibility and speed in procurement, while also attracting non-traditional partners. This essay will explore the history of OTA contracts, discuss the advantages and disadvantages compared to traditional government contracting methods, and present best practices for employing OTA contracts in R&D projects.

Concepts, roadmaps, and pipelines a plenty have been thought up to solve the innovation-to-acquisition problem. In simplest terms how do you get a product or project from start-to-sustainment? Let’s skip over all the wishful thinking about what might happen in a future DoD Acquisition reform, and instead help create capabilities today in the system we have.

While the traditional federal acquisition process can be slow and cumbersome, there are several mechanisms and approaches that can expedite the approval and funding of prototype systems. These alternative pathways can enable rapid acquisition and help accelerate the development and deployment of new technologies for various government agencies. Some of these mechanisms include: