Summarising Best Practices for Prompt Engineering

Author:Murphy  |  View: 28974  |  Time: 2025-03-23 18:30:57
Photo by Glenn Carstens-Peters on Unsplash

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"

Tags: ChatGPT Large Language Models Machine Learning Prompt Engineering

Comment

Recommend

  • ◦ Efficient Image Segmentation Using PyTorch: Part 1
  • ◦ Integrating Feature Selection into the Model Estimation
  • ◦ Effectively Use %timeit, %lprun, and %mprun to Write Efficient Python Code
  • ◦ How I Built a Custom GPT-Based Chatbot in Under 10 Minutes with LlamaIndex
  • ◦ Boosting Model Accuracy: Techniques I Learned During My Machine Learning Thesis at Spotify (+Code
  • ◦ Explore all ways to implement RLS Rules
  • ◦ Exploring Brazil's National Accounts through a Dashboard
  • ◦ LLMs for Everyone: Running the LLaMA-13B model and LangChain in Google Colab
  • ◦ Modeling the Traveling Salesman Problem from first principles
  • ◦ Efficient Object Detection with SSD and YoLO Models – A Comprehensive Beginner's Guide (
  • ◦ Introduction to Knowledge Graph-Based Recommender Systems
  • ◦ How to build an interconnected multi-page Streamlit app