LARGE LANGUAGE MODELS - Prompt Engineering

LLM Templates

LLMs generally support a concept of “templates”. Templates are pre-defined prompts that can be used to generate specific types of outputs from LLMs. They can be used to generate text in different styles, summarize long texts into shorter ones, or even generate code snippets based on natural language prompts.

GPT templates are designed to simplify the process of generating text using LLMs. They provide a consistent and effective framework for creating prompts that can elicit specific responses from LLMs. By using GPT templates, prompt engineers can save time and effort while still generating high-quality and relevant outputs.

Here are some example prompts using GPT templates:

1. Generate a product description using the “product description” GPT template for …
2. Generate a news article summary using the “news summary” GPT template on …
3. Generate a code snippet using the “code generation” GPT template that [insert what the code should do]

These prompts demonstrate how GPT templates can be used to generate specific types of outputs from LLMs. By experimenting with different templates and parameters, prompt engineers can fine-tune LLMs to generate high-quality and relevant outputs.

There are many resources available online that provide a curated list of GPT templates for various applications.

For example, the Upbase blog provides a list of over 500 ChatGPT prompts for all purposes, including product descriptions, news summaries, and code generation1. GitHub also has a repository of GPT prompt templates that can be used to create productive chat prompts for GPT models2.

If you are looking for a specific type of GPT template, we recommend searching online for curated lists or repositories that match your needs. These resources can help you save time and effort while still generating high-quality and relevant outputs.

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