Summarising Best Practices for Prompt Engineering

Prompt engineering refers to the process of creating instructions called prompts for Large Language Models (LLMs), such as OpenAI’s ChatGPT. With the immense potential of LLMs to solve a wide range of tasks, leveraging prompt engineering can empower us to save significant time and facilitate the development of impressive applications. It holds the key to unleashing the full capabilities of these huge models, transforming how we interact and benefit from them. In this article, I tried to summarize the best practices of prompt engineering to help you build LLM-based applications faster. While the field is developing very rapidly, the following “time-tested” :) techniques tend to work well and allow you to achieve fantastic results. In particular, we will cover: <ul> <li>The concept of iterative prompt development, using separators and structural output;</li> <li>Chain-of-Thoughts reasoning;</li> <li>Few-shot learning.</li> </ul> Together with intuitive explanations, I’ll share both hands-on examples and resources for future investigation. Then we’ll explore how you can build a simple LLM-based application for local use using OpenAI API for free. We will use Python to describe the logic and Streamlit library to build the web interface. <a href="https://towardsdatascience.com/summarising-best-practices-for-prompt-engineering-c5e86c483af4">Read More</a>