5 Frameworks to Guide Better Reasoning in Models (Development of Large Model Applications 7)
How to Enhance AI Model Accuracy: 5 Proven Prompting Techniques
Hello everyone, welcome to the "Development of Large Model Applications" column.
Order Management Using OpenAI Assistants' Functions(Development of large model applications 2)
Thread and Run State Analysis in OpenAI Assistants(Development of large model applications 3)
Using Code Interpreter in Assistants for Data Analysis(Development of large model applications 4)
5 Essential Prompt Engineering Tips for AI Model Mastery(Development of large model applications 6)
In the last lesson, we demonstrated 5 fundamental principles for interacting with AI models through 5 practical examples.
You should now realize that well-designed prompts can greatly improve the quality of AI model outputs.
Using different models, we also saw their unique features and suitable scenarios.
In this lesson, we'll focus on a higher-level prompting method—setting up thinking frameworks to help AI models perform more organized and reliable reasoning.
Few-shot, designed to enable models to learn and generalize new tasks from minimal data samples.
Chain of Thought, is a typical solution that builds prompts through a thinking framework, guiding the model to complete complex tasks.
Today, we'll continue with few-shot and Chain of Thought to explore how to design thinking frameworks to guide AI models in deep, comprehensive reasoning and improve performance on various complex tasks.
Additionally, we'll introduce other thinking frameworks like self-consistency, reflection, and dialogue.
Through these methods, AI models can not only provide answers but also explain their reasoning process and even question and improve their own thoughts.
From these thinking frameworks, you'll see the academic and industrial efforts in enhancing large models through prompts, exploring, and experimenting from different angles.
Let's begin our advanced journey into prompt engineering.