Where is generative AI genuinely useful?

I am an AI skeptic. For a lot of reasons. But mainly, because lasting tech innovation tends to come from the bottom. Engineers adopt something because they think it’s fun or neat or saves them time, then one day, someone has a light-bulb moment and turns it into a product.

This is too simple, but in broad strokes, it’s been the story with cloud computing, blogs, and all kinds of Python libraries and open source tools that are now foundational to modern computing.

However, with generative AI, it has been just the opposite: A handful of companies with more money than God are piling into it and making the technology available to the general public before it’s even a product. They are selling $10 bills for $2 with basically no idea how to turn that around.

So far, the technology’s most popular uses are actually destructive: fraud, impersonation, disinformation, academic cheating, non-consensual porn. Polluting human networks with AI-generated slop and destroying the online job market with automated spam tools. All popular uses, certainly, but nothing Microsoft can (publicly) monetize.

Fine, fine. We all know this. The question for myself, the AI skeptic, is what are its actual legitimate uses for me, a tech guy, and what does that say about its future sustainable business uses?

For me, the answer is onboarding.

I use ChatGPT extensively to learn about software systems, and this has been hugely helpful as I develop new skills and “onboard” myself as a web developer. Software documentation for complex frameworks like Django is dense and assumes you already understand a vast nested list of other concepts that maybe you don’t, which makes it very slow going.

But ChatGPT has that documentation (and a million other tutorials and explainers and Stack Overflow threads) in its training data, so I can ask a question like “what are the best practices for adding an editor feature to my Django blog?” and it will give me a rundown of the the settings, models, and views one would need to get started on that feature, along with examples.

Then I go to the documentation and read up on the particular bits, and I can always ask follow-up questions to ChatGPT if there’s a concept I don’t understand.

This isn’t using an LLM for code generation, it’s using it to access and sift a repository of knowledge, and the chat format works really, really well for that.

Now, I am aware that there are drawbacks to using an LLM for research. It occasionally makes things up, it is over-confident, it tells you what it thinks you want to hear, etc. But from what I’ve seen over the last couple years, these are manageable problems that can be controlled, and the benefits of being able to get quick answers to complex and very specific queries far outweigh the costs of having to take them with a grain of salt.

I don’t actually think this means that AGI is upon us and the robots will take our jobs. Rather, custom LLMs that are fine-tuned or use resource-augmented generation (RAG) to give employees interactive access to manuals, code bases, sales histories, etc. would be very powerful tools for onboarding and internal knowledge sharing.

However, there are a couple issues I can see.

First, this is expensive, and it’s not clear yet that customers are willing to pay the actual cost of running a giant LLM like ChatGPT-4o. Second, it’s very tricky to get your actual data into a form where it can be used for fine-tuning so users can have the kind of experience I am having now with ChatGPT. Here’s how one Reddit user describes it, and it sounds like a nightmare:

However, the real challenge lies in preparing the data. A massive wiki of product documentation, a thousand PDFs of your processes, or even a bustling support forum with countless topics – they all amount to nothing if you don’t have your data in the right format.

Automated tools can only do so much; manual work is indispensable and in many cases, difficult to outsource. Those who genuinely understand the product/process/business should scrutinize and cleanse the data. Even if the data is top-notch and GPT4 does a flawless job, the training could still fail. For instance, outdated information or contradictory responses can lead to poor results.

In many of my projects, we involve a significant portion of the organization in the process. I develop a simple internal tool allowing individuals to review rows of training data and swiftly edit the output or flag the entire row as invalid.

Yikes! Now imagine doing this every six months to keep the LLM up to date. Employees will hate it! RAG is easier, but it’s still tricky and may not provide the same experience.

All that said, I think these tools are going to get better, faster, and cheaper, and within the next few years, most large enterprises are going to have some kind of onboarding or search LLM available internally.

This is great news if you work in IT and were worried about job security, because setting up and maintaining these things is going to become a whole new sub-field. What if AI is a big job-creation program??

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