Artificial intelligence has rapidly evolved from being a technological curiosity to a driving force in the business world. But despite its power and seemingly intuitive interface it can be hard for new users to really put AI’s capabilities to work.
Tools such as Chat GPT, and other large language models (LLMs) are at the forefront of this breakthrough, their capabilities extending far beyond previous programmed systems.
These AI models possess the capacity to “understand” and generate human-like text from simple instructions, making them invaluable in a wide array of applications, from content creation and design to coding and data analysis.
But for new users to put their capabilities to work requires both user precision and optimisation.
The fact that these programmes have been trained on a massive volume of data makes AI systems exceptionally powerful tools. It is now possible to extract data from documents and to potentially automate workflows across many areas of business and across most industries.
However, to achieve optimal results the AI system requires clear instructions. This is where prompts — the written instructions a user deploys to create outputs — come into play.
As I discussed in my recent post on prompt engineering, a prompt is essentially a set of instructions or a question that guides the AI in generating a desired response. A vague or poorly phrased prompt may yield inaccurate, hallucinatory or irrelevant results, while a well crafted prompt can deliver an incredibly useful result.
Adept users will also know how to follow up on their initial instructions to refine the output.
Which is why well-crafted prompts are just one half of the equation. The other half is fine-tuning, or the process of customising an AI model to produce results that align more closely with your specific requirements.
Fine-tuning ensures that the AI becomes a valuable tool tailored precisely to your needs. Specifically, fine tuning is useful for:
Task specificity: Fine-tuning enables training AI models for specific tasks such as legal research or financial analysis.
Quality control: It allows for quality control, critical in industries like health care and finance where accuracy is paramount.
Personalisation: Fine-tuning permits the personalisation of AI responses to match a brand’s tone, style, and values, ensuring a consistent customer experience.
Efficiency: Fine-tuned AI models are more efficient and require fewer iterations, saving time and resources.
Put simply, fine tuning the system makes it easier for users to elicit the results they’re looking for, and businesses need to invest in both to get the most out of generative AI systems.