Knowledge

As most of us within the tech services industry, we've been looking into how we can leverage AI for our existing and new clients.

Before diving into the opportunities, let's make a short summary of the state of things.

AI, or as we prefer to call it, LLMs are primarily focused on the following areas: text, images and in a later stage video & sound. Albeit, at the time of writing this article, video & sound are not yet fully public.

When we talk about AI, we prefer to reference to them as LLMs because, in current AI jargon, we actually have other areas as well. In the past decade, AI has been referred to machine learning, data mining, neural networks, and even some areas of business intelligence. LLMs, are just the newest additions to the field and who knows, what newer technologies will emerge in the near future.

For the purposes of this article, when we talk about AI, we actually refer to LLMs.

So how can LLMs help your business?

We've identified the following main areas.

Applications — Text Processing

LLMs have been proved successful in doing text processing, either in the form of text summarisation, data extraction and data processing. All of these "activities" or "tasks" for the AI can be output via various channels. The most obvious one is in the form of a chatbot, but other channels can be also implemented as well: form-based webpages or UI applications.

Companies that have large sets of proprietary data such as prospects, internal documents, product descriptions can use LLMs in the following ways:

• Text Summarisation — the ability to compress various texts in fewer words that highlight key points.
• Data Extraction — extract data directly into CSV, JSON or Excel format from various data sources such as CVs, prospects, web pages, etc.
• Data Processing — the ability to "comprehend" a text and provide a novelty answer that was not (necessarily) verbose in the input data.
• Data Validation — incorporate LLMs into any existing (or new) data validation workflow.

Text Generation

Probably the most famous feature of current LLMs was the ability to generate text, or better say, content. This is made possible by the large data set that was feed into the model which based on a "prompt" is able to create answers.

Some applications have already made their way into business realm:

• Code Assistants — Tech companies and developers have embraced LLMs to help them generate code templates, find bugs or improve existing code with great results.
• Copywriting — LLMs are starting to be used to generate ads, documents, reports, and other business content.

Pros & Cons

Costs: At the moment, we think cost is the most important factor to consider. While the "currency" of an LLM input is rather cheap right now (price per token), when we talk about business applications used by businesses, the costs can easily compound to thousands of dollars when several people do queries in the LLM.

Private LLMs vs Public LLMs: Not many organizations are willing to allow their proprietary data to be fed into LLMs. As such, more businesses will be willing to deploy their own private LLMs running on their own hardware, which further increases the cost.

Hallucinations: It is very well documented that LLMs have hallucinations — their "disability" of generating false/fake references. However, there are ways to limit the impact of hallucination.

We hope this article provides with enough information to help you generate ideas how LLMs could be useful for your organization.