A Deep Dive into Prompt Engineering Techniques: Part 1
Large Language Models (LLMs) are widely available and easily accessible and are increasingly a part of business. Whether you’re interacting with an LLM via the provided interface or connecting via an API and integrating it into other systems, it’s helpful to understand how to get the best possible results out of the model.
Prompt Engineering is a technique that focuses on perfecting your input to get the best possible output out of the language model. Of all the different techniques available to get LLMs to fit your use case best, it’s the most straightforward one to implement since it focuses primarily on improving the content of the input. In this Part I article, we’ll dive into different Prompt Engineering techniques and how to leverage them to write highly effective prompts, focusing on single prompt and chain techniques. In our following article, we’ll cover agents and multi-modal techniques.
For other available techniques to enhance LLM capabilities, check out our Techniques to Enhance the Capabilities of LLMs for your Specific Use Case article!
New to LLMs? Check out this article on the landscape by our friends over at Shift: Guest Post: Navigating the AI Chatbot Landscape.
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