Prompt engineering

Prompt engineering is the art of crafting well-structured queries to obtain accurate and relevant responses from large language models (LLMs). The better the prompt is formulated, the higher the quality of the response. There are many prompting techniques, but we will focus on several of the most common ones. Here are five key techniques that can help improve your interaction with AI:

1. Zero-Shot Prompting – zero context. The model is given a task without any examples, relying solely on its prior training. This technique is quick and convenient but may sometimes lead to less accurate results.

2. Few-Shot Prompting – a few examples. By providing the model with several samples of the expected responses, you can significantly enhance its understanding of the context and receive a structured answer in the desired format.

3. Chain-of-Thought Prompting – chain of reasoning. Encourages the model to explain its logical process step by step. This is especially useful for complex calculations, analytics, and problem-solving that require a multi-step approach.

4. Meta Prompting – prompt self-improvement. This method of prompt engineering uses large language models (LLMs) to create and refine prompts. Unlike the traditional approach, where the prompt is manually crafted without the possibility of adjustment, meta prompting allows the LLM to dynamically adapt and improve the prompt based on your feedback.

5. Self-Consistency – self-consistency. Instead of generating a single response right away, the model produces multiple answers and compares them. The most appropriate one is then selected, reducing the chance of random errors. It works like a group decision-making process: the more people arrive at the same conclusion, the higher the chance that the answer is correct.

Why is this important?
Competent use of these techniques can significantly improve the outcomes of working with LLMs by ensuring accuracy, logical coherence, and alignment with expectations.