How to create useful AI applications

To create useful AI applications you need to understand how AI works and how it can best be used.

In this article we explain AI and give insights on how to create custom applications. You will see what is possible with AI out-of-the-box. You will learn what the limitations are and how to overcome them. And maybe you will get ideas on how AI applications can be used in your work.

To inspire you we will show you some engaging examples of how we have integrated LLMs in different applications at the end of this article.

What is AI?

AI stands for Artificial Intelligence, which refers to the simulation of human intelligence processes by machines, especially computer systems. Usually when we refer to AI we are actually talking about GPT applications.

GPT stands for Generative Pretrained Transformers and refers to a type that utilises a transformer architecture. This is a deep learning model architecture primarily used for natural language processing tasks.

How do Large Language Models work?

GPT models are trained on large amounts of text data and are capable of generating human-like text based on the input they receive. Due to the large amount of data these models are trained on, they are also referred to as Large Language Models or LLMs.

The "pretrained" aspect in the acronym GPT refers to the fact that these models are initially trained on a vast amount of text data using unsupervised learning techniques. During this pretraining phase, the model learns to predict the next word in a sequence of text given the preceding context. This allows the model to capture a wide range of language patterns and semantics.

The "generative" aspect in the acronym GPT signifies that these models can generate coherent and contextually relevant text based on a prompt or input provided to them. They achieve this by using the knowledge learned during pretraining to predict the most probable next word or sequence of words given the input context.

GPT models are trained on large amounts of text data and are capable of generating human-like text based on the input they receive. Due to the large amount of data these models are trained on, they are also referred to as Large Language Models or LLMs.

The "pretrained" aspect in the acronym GPT refers to the fact that these models are initially trained on a vast amount of text data using unsupervised learning techniques. During this pretraining phase, the model learns to predict the next word in a sequence of text given the preceding context. This allows the model to capture a wide range of language patterns and semantics.

The "generative" aspect in the acronym GPT signifies that these models can generate coherent and contextually relevant text based on a prompt or input provided to them. They achieve this by using the knowledge learned during pretraining to predict the most probable next word or sequence of words given the input context.

When you want to create an AI application, think of the LLM as a software that can “talk”, meaning it can create grammatically correct sentences that have a high probability of making sense.

Of course such a model can only talk about what it “knows”, meaning what it was trained on.

How are LLMs trained? Is it useful to do this myself?

Training a Large Language Model can be a challenging and resource-intensive task. It requires substantial computational resources, including powerful GPUs and a distributed training infrastructure. Training models with millions or billions of parameters is expensive and time-consuming.

Also large amounts of high-quality text data from diverse sources is needed and collecting and preprocessing such datasets typically requires significant effort to ensure data quality and diversity.

However, if sufficient infrastructure, resources, time, and data are available training is typically done using a combination of unsupervised and supervised learning techniques. As a result the intrinsic semantic and logical relationships between the elements in a dataset are discovered and are used when generating responses to data input.

However, there are a lot of pre-trained general purpose models available that can be used instead of training your own model. The most famous ones are from OpenAI and Mistral AI.

Usually, it is much more efficient to use a pre-trained model in your application in comparison to training a model with your own data.

How can I best use existing LLMs?

Large Language Models can only "talk" about topics (data) that they were trained with.

In order to answer questions about different topics this data has to be passed to the model before asking the question. This process is referred to as "in-context learning".

To enable your AI to talk about a specific topic you have to pass the relevant information to the model before interacting with it.

Besides passing data to a Large Language Model you typically pass instructions to a data model describing how the model should respond.

Examples of such instructions are the tonality you want to use or restricting the topics your application should respond to.

This article is written by Dr. Christoph Breidert and was first published on the 1xINTERNET website - check here for more highlights.