AI prompting is typically divided into two parts, the system prompt and the user prompt. The former is the prompt instruction which is embedded inside a model as part of the design process.
This system prompt defines the purpose, design and operational parameters of how the AI model will react to user input.
As such, it is a crucial part of the utility of an AI model, particularly when that model is applied to specialist uses, as with fine-tuning.
The user prompt is the means by which a user makes a request to an AI model. The request is typically made either by typing into a text box or speaking into a microphone, which is then converted by the system into text for the model to understand.
This is human machine communication at its most basic level. These instructions are then used by the AI system to identify, evaluate and deliver a relevant and valuable response back to the inquirer.
Why is prompting important?
What makes AI prompting so important in this interaction is the fact that any AI response is very much governed by two main factors.
First the type of model that is being interrogated and the quality of its system prompt, and secondly the content of the user prompt and the way it is structured.
A good user prompt can elicit an extremely valuable response from the model, while a bad prompt may result in vague, hallucinated or even downright wrong answers.
The art of prompt engineering has grown up around the need for user prompts to be accurate, clear and unambiguous. Often, when non-experts complain about the lack of quality in an AI response, it is because of the user’s inability to clearly state what they want from the model in the first place.
Clarity is everything
For example a user may ask “where’s the best place to buy apples“? For the AI this vague question immediately presents a number of different possible answers. Does the user mean apples as in the delicious fruit, do they mean Apple computers or are they in fact looking to purchase an Apple iPhone?
Even this simple example could result in a doubling of the compute time needed to identify the exact meaning of the user request before giving an answer.
This problem is magnified exponentially once we move into the realm of complicated prompts, which demand complex calculations from the model.
Precise and accurate prompts lead to optimum AI outcomes, and vice versa. Garbage in, garbage out.
The main problem is that modern AI models are designed to process enormous amounts of information as part of their decision process. So every ambiguous request could result in a large amount of unnecessary processing of unimportant detail. This can waste valuable compute time and money.
The importance of good prompting in research
Good quality AI prompting is also important for sophisticated tasks like research. This is especially true in areas like academia or healthcare, where imprecise prompting can result in disastrous output from the model, endangering lives or careers.
In these cases absolute clarity in the prompt is essential, not only to elicit the right response, but also to make it easy to understand.
The good news is that newer models are becoming much more adept at sifting the best out of any prompt irrespective of its quality.
This is especially noticeable with reasoning models, which employ chain of thought methods as a part of their output process. In these cases the best models will typically ask the user additional clarifying questions to clear up any potential misunderstanding.
Despite these improvements, the work of prompt engineering continues to be an essential part of almost all significant large-scale AI integration projects.
However there’s no doubt that as AI quality improves, prompt engineering will become less of a factor.
We’ve already seen the market demand for prompt engineers reduce as the models mature, and this trend is only likely to continue.
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