Many of us are familiar with generative AI models such as Chat GPT. We will have asked many questions, received answers and used those answers for a variety of applications.
Before getting into prompt engineering, it’s important we firstly understand how AI searches work. AI models generate responses based on their training data, which consists of a mixture of licensed data, publicly available text, and human-provided information. They do not create new information.
Since AI models are merely souring existing information for answers, the possibility of those answers being wrong is palpable. Without effective prompt engineering, AI-generated responses may contain bias, outdated information, or lack accuracy.
That might not be a problem for someone trying to win an argument in the pub. However, when millions of pounds are being poured into long-term strategic decisions…well, you can imagine…
This is why Prompt Engineering has become such a sought-after expertise in recent years.
Prompt Engineering refers to the art of procuring specific and accurate information from AI models.
If this sounds simple, it isn’t. In this blog, we explain why we deliberately describe this as an art…
What is prompt engineering?
Prompt engineering involves understanding how generative AI models such as ChatGPT processes data sets, and using this information to strategically formulate questions, or ‘prompts’ to elicit specific responses.
Why is prompt engineering important?
Without prompt engineering, you are in danger of eliciting incorrect answers. They may contain bias or opinion. They may be out of date. They may not be on brand, effectively not offering any utility to your business.
At the extreme, you could be in danger of being uncompliant, of transgressing rules and regulations. Even though generative AI is fairly new, there are already countless examples of damage being wrought by not using prompt skills.
The chances are you are planning to utilise AI for various tasks such as question answering, data research, text summarisation or code generation. In all of these instances, there is a risk to using AI that can only be avoided by applying prompt techniques such as the following which are listed in order of complexity and therefore accuracy:
- Direct Prompting (Zero-shot): This is where an instruction is supplied to the model without any examples.
- Prompting with Examples (One-, Few-, and Multi-shot): As the name suggests, this is where the model is given examples of the desired input-output in order to support more contextualised reasoning.
- Chain-of-Thought Prompting: This a more advanced technique, where the model is required to explain its reasoning on a step-by-step basis, improving accuracy and understanding.
- Tree-of-Thought Prompting: A more advanced technique which allows the model to explore multiple potential branches of reasoning with a view to provide more creative and robust answers
Conclusions
If your business is beginning to use Artificial Intelligence to make key decisions, then this is a specialist job that needs to be carried out by a prompt engineer. If not, you run the risk of introducing incorrect information into your workflows. Here are some examples of where prompt engineering expertise is needed:
- If you are looking to make marketing decisions based on competitor analysis.
- If you are making pricing decisions based on market valuation or supply chain.
- If you are developing code for a new application or website.