Generative AI, especially with the surge in attention following ChatGPT's launch, has captured the imagination of businesses worldwide. But while the hype has successfully thrust AI into the mainstream, the real question is: how can organisations move beyond surface-level use and truly harness Generative AI to drive meaningful efficiency and innovation within their operations? To unlock its full potential, we must first delve into what GenAI really is and how it can be integrated effectively into business strategies.
GenAI is a form of artificial intelligence that focuses on creating new content, such as text, images, video, audio, or code, by learning from existing data. The foundation of GenAI is based around Large Language Models (LLMs). Although language models have been around since the 1960s, with the earliest example being a program called Eliza, the difference today lies in the sheer size and capability of these models. For more information, see The Simple Guide to Large Language Models (LLMs).
We interact with GenAI by providing input through a natural language interface, typically in the form of a chat window that integrates with an LLM. Currently, many people use GenAI as if it were a search engine. However, this approach is limiting and underutilises the capabilities of LLMs. To maximise the potential of GenAI, it’s essential to know how to query it effectively. This has led to the development of a new discipline called prompt engineering.
Prompt engineering is a set of techniques designed to help users extract more useful information from an LLM. One effective framework for prompt engineering is the TRACi framework:
In addition to the TRACI framework, there are other techniques to obtain better results from LLMs:
Using GenAI effectively requires consideration of these factors, which can be a lot to ask of the average user. Therefore, for widespread adoption, the use of GenAI needs to be made simpler and more intuitive for end users.
One area where we see momentum gathering is the adaptation of Large Language Models (LLMs) for specific use cases or industry sectors. These systems are integrated with an LLM like OpenAI (the model behind ChatGPT) but are enhanced with additional data specific to the sector or company.
For example, a company called Harvey uses OpenAI but has fine-tuned a version specifically for legal purposes. Harvey has also designed an intuitive front-end interface, allowing users to carry out specific tasks such as drafting documents, conducting legal research, and performing analysis etc all through a user-friendly UI. The uptake has been phenomenal, with many global legal firms now using it.
Many companies have started adopting similar approaches. For instance, Bloomberg and Deloitte have customised GenAI models, and PwC has developed its own GenAI tool called ChatPwC These internal systems integrate with LLMs behind the scenes but are tailored to key use cases specific to their business.
We believe that the future of GenAI lies in each company developing its own AI system, which will effectively become the 'brain' of the organisation. These systems will provide employees with user-friendly UI access to tools for specific tasks relevant to the company’s functions.
Ready to unlock the full potential of Generative AI for your business? Reach out today to learn how we can help you create your own customised AI solutions that drive efficiency and innovation!
