Once you've used llm-prepare your flat or set of flat files, you can use the output with these example prompts.
- Coding: A set of interactive code related prompts.
- Code Review: Interactive code review with a simulated senior software engineer.
- Technical Document Generation: Interactive technical document generation with a simulated senior technical writer.
- Test Generation: Interactive test generation with a simulated senior software engineer and simulated QA.
- CSV: A set of interactive CSV related prompts.
- Extract Named Entities: Interactively extract user defined named entities.
- Generate Chart (Experimental): Use GPT-4 and DALL-E to generate charts based on CSV data. This prompt is experimental.
- Question and Answer: Ask questions and get answers from your CSV data.
- Coding: A set of simple coding oriented prompts.
- C# XML Documentation: Generate C# XML Document format comments.
- Js JSdoc Comments: Generate JSDoc format comments.
- PHP PHPdoc Comments: Generate PHPDoc format comments.
- Python DOCstrings Comments: Generate Python DOCstrings format comments.
- Readme Generation: Quickly generate a README.md for your project.
- Ruby Yard Comments: Generate Ruby YARD format comments.
- Rust RUSTdoc Comments: Generate Rust RUSTdoc format comments.
- Typescript TSdoc Comments: Generate TypeScript TSDoc format comments.
- CSV: A set of simple CSV oriented prompts.
- Extract All Named Entities: Extract all named entities, including People, Organizations, Locations, Companies, and Products from the provided data.
- Generate MySQL CREATE TABLE: Generate a MySQL CREATE TABLE statement based on the context of the data.
- Generate Summary: Show the breakdown of the data structure, and provide a human readable summary of the information based on the provided data.
- Identify Missing Column Titles: Identify missing column titles based on the context of the data.
All example prompts have been tested with ChatGPT GPT-4 but are not guaranteed; LLM updates can result in changes to the output of prompts.