Prompt Engineering: Easiest Yet Strongest Way to Conquer LLMs

Have you heard the saying that English has now become the hottest programming language? You better have. Prompt engineering, the art on how to prompt LLMs better using natural language, has recently emerged as a pivotal skill set. Being a skilled prompt engineer means you’re at the core of current technological tends, giving you a significant advantage in terms of job opportunities, quality of life, and more. Plus, as mentioned earlier, it’s a newly discovered area, so why miss the chance to get ahead by being among the first to explore it?

Understanding Prompt Engineering

Prompt engineering involves more than just entering text; it’s about effectively communicating with an AI, guiding it to understand and accurately execute tasks. This requires a nuanced understanding of the model’s language and capabilities. The goal is to create prompts that are clear, concise, and contextually rich, enabling the AI to respond in the desired manner, which is crucial as such LLMs sometimes do not behave as expected.

How In-Context Learning Works

In-context Learning, or ICL, in short is to provide examples to the model. The term may change to n-shot learning, or few-shot in general, depending on the number of exemplars you use. For example, to make the model calculate the average of given numbers, you might prompt it like this

20, 25, 30: 25
17, 15, 17: 16.3
16, 19, 12: 

Then you’re doing 2-shot learning, giving the top two as exemplars and the bottom one as the actual problem you want answer.

Strategies for ICL

ICL is a powerful technique for eliciting intended actions from an LLM, especially when the task is specific and requires expertise that the LLM might not possess. So here are 5 tips you should consider.

1. Clear Task
   Begin by clearly defining the task. Description on whether it’s generating text, answering a question, or creating content should be very helpful.
2. Reasonable Examples
   ICL highly depends on the examples. You need to pay close attention when choosing examples and you may be required to process them in a form favorable for the model.
3. Consistent Format
   Maintain a consistent format for your examples and queries. This helps the AI understand the pattern and apply it to generate responses.
4. Contextual Richness
   If the task is complex, consider providing more context to help the AI grasp the nuances of the task. This might include background information, specific constraints, or desired outcomes.
5. Iterative Refinement
   Based on the AI’s responses, refine your prompts to enhance clarity and context, gradually honing in on the most effective formulation.

Privacy Issues of Prompt Engineering

Consider a scenario where you need to determine the presence of diseases in patients based on their health records. It’s unlikely you’d use LLMs for this task. LLMs aren’t medical experts, and while they can’t provide diagnoses, sharing sensitive data as examples is not advisable due to privacy concerns. Trusting the model, its managing company, and any intermediaries might not be viable. Building your own model might be necessary, but this requires significant expertise. What should we do then? Use synthetic data instead.

Synthetic Data as the Problem Solver

Synthetic data offers a notable benefits in terms of privacy, copyright, and so on. And it’s proven that using synthetic data in place of real data does not compromise the performance.