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GPT-3, GPT-3.5, GPT-4, and GPT-4o models from OpenAI are prompt-based. With prompt-based models, the user interacts with the model by entering a text prompt, to which the model responds with a text completion.
Do you want to maximize the potential of your favorite large language models? Prompt engineering is a critical skill for building intelligent apps with generative AI. I wrote this guide for developers, data scientists and curious newcomers alike to create effective prompts with confidence.
This document covers advanced prompt engineering techniques, optimization strategies, and production best practices for Azure OpenAI applications. It builds upon the basic concepts introduced in Basic Concepts and extends the parameter configuration knowledge from Parameter Configuration.
Prompt engineering has become a crucial skill in the age of large language models (LLMs) and generative AI. It's the practice of crafting effective inputs to guide AI models towards generating desired outputs.
While the principles of prompt engineering have a broad applicability across different model types, specific models like Azure OpenAI GPT models call for unique prompt structures. In this article,...
The focus of this post is on prompt engineering and how you can refine the inputs you provide to OpenAI models to produce more relevant responses. Let’s get started! What is a prompt? A prompt is the user input provided to a model to generate completions. The prompt is what guides the model to generate responses known as completions.
The system message is included at the beginning of the prompt and is used to prime the model with context, instructions, or other information relevant to your use case. You can use the system message to describe the assistant’s personality, define what the model should and shouldn’t answer, and define the format of model responses.
In this article, I will share some tips and best practices for prompt engineering with Azure Open AI. I will also show you some examples of how to use prompts for different tasks and...
Prompt Engineering is the process of adding additional context to the prompt to provide "grounding" to the AI model and make it more likely to produce the desired response and less likely to produce undesirable outputs.
OpenAI’s O1 and O3-mini are advanced “reasoning” models that differ from the base GPT-4 (often referred to as GPT-4o) in how they process prompts and produce answers. These models are designed to spend more time “thinking” through complex problems, mimicking a human's analytical approach ,