Domain-Specific LLMs: The Future of Private AI-Powered Content Generation

Reflecting on my experience at the GenAI summit, I became convinced that domain-specific Large Language Models (LLMs) will revolutionize how we generate private content. In this blog post, I'll dive into private LLMs and explore their potential to summarize, create blogs, perform vector searches, call functions, and more.

Why Domain-Specific LLMs Matter

Imagine a future where corporations can use AI-powered content generation without compromising their sensitive data. Domain-specific LLMs promise this privacy—the ability to train models on private data and use private data in prompts while maintaining control over the generated content and protecting the data. With the rise of publicly available LLMs, there's growing concern about data exposure and misuse. Domain-specific LLMs offer a solution by allowing organizations to keep their data private, secure, and relevant to the needs of a company's employees.

My Experience with Local LLMs

As I've been exploring this space, I decided to accelerate my research into domain-specific LLMs. I wrote an application that leverages local LLMs for various tasks, including summarization, blog generation, vector searches, and function calls.

When generating a blog, for example, the program provides a comprehensive prompt that guides the LLM on formatting, structure, and more. This blog post was created by me and founded on a blog generated using a local LLM, followed by grammar checking and finishing touches from me. When I use LLMs to create a one-page blog, I give the LLM a significant amount of input on the ideas to be covered in the blog, and I give it over a page of input on how to structure and format the blog. Hence, there is consistency from blog to blog - something all brand marketers care about.

Many marketing people have the view that LLMs generate generic crap - which can be true when marketers give the LLM a prompt that is generic crap.

The Benefits of Domain-Specific LLMs

Local LLMs may be slower than publicly available models, but they offer unparalleled control over the data used for training. This means that organizations can maintain the integrity of their private information while still benefiting from AI-powered content generation. In the future, I plan to explore alternatives, including Internet-accessible LLMs with private model establishment capabilities.

For further research

The application I wrote can use any of many local LLMs. I just happened to choose LLAMA3 (Meta) for this blog, but I can easily switch to phi3, codellama (that specializes in creating programming code), and more.

The importance of this is that marketing teams will more than likely use LLMs trained specifically for marketing activities, and potentially even for their specific brand. There are already commercial offerings along these lines, however, here I am drawing attention to the use of freely available models.

As mentioned above in the image about the 40+ seconds it took to produce the foundation of this blog, that is much slower than what we all have become used to from publicly available models. I have seen commercially available servies from companies like deepinfra that are much faster, but I need to look at what kind of guarantees they provide to corporates. At 60 cents per million tokens, they might be usable.

Conclusion

Domain-specific LLMs have the potential to transform the way we generate private content. By leveraging local models and controlling the data used for training, organizations can safeguard their sensitive information while still harnessing the power of AI. In my next blog post, I'll delve deeper into Internet-accessible LLMs with private model establishment capabilities. Stay tuned! I will also provide some more technical details in the tech newsletter.

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