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The following is a quick ramble before I get into client work, but might give you an idea of how AI is being used today in companies. If you have an questions about Generative AI, let me know!

The work to make the OpenAI API (built on Nelson Ferraz's OpenAPI::Client::OpenAI module) is going well. I now have working example of transcribing audio using OpenAI's whisper-1 model, thanks to the help of Rabbi Veesh.

Using a 7.7M file which is about 16 minutes long, the API call takes about 45 seconds to run and costs $0.10 USD to transcribe. The resulting output has 2,702 words and seems accurate.

Next step is using an "instruct" model to appropriately summarize the results ("appropriate" varies wildly across use cases). Fortunately, we already have working examples of this. Instruct models tend to be more correct in their output than chat models, assuming you have a well-written prompt. Anecdotally, they may have smaller context windows because they're not about remembering a long conversation, but I can't prove that.

Think about the ROI on this. The transcription and final output will cost about 11 cents and take a couple of minutes. You'll still need someone to review it. However, think of the relatively thankless task of taking meeting minutes and producing a BLUF email for the company. Hours of expensive human time become minutes of cheap AI time. Multiply this one task by the number of times per year you have to do it. Further, consider how many other "simple tasks" can be augmented via AI and you'll see why it's becoming so powerful. A number of studies show that removing many of these simple tasks from people's plates, allowing them to focus on the "big picture," is resulting in greater morale and productivity.

When building AI apps, OpenAPI::Client::OpenAI should be thought of as a "low-level" module, similar to DBIx::Class. It should not be used directly in your code, but hidden behind an abstraction layer. Do not use it directly.

I tell my clients that their initial work with AI should be a tactical "top-down mandate/bottom-up implementation." This gives them the ability to start learning how AI can be used in different parts of their organization, given that marketing, HR, IT, and other departments all have different needs.

Part of this tactical approach is learning how to build AI data pipelines. With OpenAI publishing their OpenAPI spec, and with Perl using that, we can bring much of the power of enterprise-level AI needs to companies using Perl. It's been far too long that Perl has languished in the AI space.

Next, I need to investigate doing this with Gemini and/or Claude, but not now.

Note, if you're not familiar with the BLUF format, it's a style of writing email that is well-suited for email in a company that is sent to many people. It's "bottom-line up front" so that people can see the main point and decide if the rest of the email is relevant to them. It makes for very effiicient email communication.

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