The advent of General-Purpose Transformational (GPT) models, followed by coding assistants like GitHub Copilot, has revolutionized the software engineering landscape. However, many developers struggle to go beyond basic use cases. A bit of creativity is needed. To unleash the full potential of AI in your daily work, I’ve compiled a comprehensive list of 50 AI mini use cases that you can apply using general GPT or a coding assistant.
Before diving into these use cases, please keep two essential points in mind: first, ensure you have the necessary permissions to input information into the chat bot from a security perspective; and second, always review the output generated by the AI tool.
GPT Use Cases
This is for use cases outside of your integrated development environment (IDE). It contains simple things that you probably did manually in the past. The order is random.
- Translate: use it to understand any text.
- Test data generation: based on classes or examples.
- Dashboard explanation: export a monitoring or analytics dashboard source. Paste it into the chat and ask it to describe the dashboard in natural language, including where the data comes from and how numbers are calculated.
- Query parameter generation: paste a JSON and ask it for transforming into a query parameter string.
- Test scenarios: generate test scenarios based on a feature description.
- Drafting documents: draft documentation and manuals.
- SQL queries: write or optimise SQL queries.
- Write queries for your monitoring system, e.g. Splunk or Grafana.
- Summarise feedback: textual user feedback and suggest improvement ideas.
- Release notes: Generate release notes based on GIT commits.
- Scripts: Write simple scripts.
- Meeting notes: Summarise meeting notes and create a task/action list.
- Data validation rules: Given a dataset schema, generate a set of data validation rules (e.g., 3-5 rules) to ensure the data meets certain criteria and is consistent with the schema.
- Draft presentations: create a first version or an outline for a presentation.
- Review architectures: describe how you plan to implement a feature on the architectural level to get feedback, descriptions or diagrams.
- Generate checklists: create checklists for deployments, releases or other critical tasks.
- Evaluate skills: explain your current skills and ask for an evaluation and list of gaps to guide your next learning steps.
- Summarise technical articles: to learn about topics.
- Quizzes: generate a quiz based on a technical article or documentation.
- Interview preparation: ask for potential questions based on a job description.
- Create survey or forms: generate questions and answer options. Also use it later to summarise replies.
- Glossary: generate a list of terms that are not generally understandable.
- Generate the agenda for a meeting: draft the meeting invitation.
- Explain a dataset: take a dataset and explain the latest trends and highlights.
- Vulnerability analysis: describe a setup or solution and ask for a threat modelling and potential vulnerabilities.
These were the general chat use cases. Some of them could also be done in the IDE directly of course. Anyway, in the next chapter you find use cases that are more typical to be directly executed by your AI coding assistant.
Coding Assistant Use Cases
The use cases below are all about using CoPilot and co. in a way to help you working.
- User Story to code: copy a complete User Story description into the chat, use agent mode, and ask it to generate the code for the story.
- Review code changes: at the end of implementing a change, ask the coding assistant to review your changes for security, clarity, potential bugs or other issues.
- Visualise logic: open a class or module that contains lots of logic. Ask to generate a mermaid diagram definition (text based diagram).
- Regex: ask to generate a regex based on a textual description or examples.
- Generate specific values: generate UUIDs or other random data that you occasionally need.
- Tests: ask to generate unit tests for a certain class or module.
- TDD: generate the implementation based on tests.
- Test coverage: ask which cases are not tested yet.
- Error explanation: paste a stack trace of an error and ask for a fix.
- Data transformation: transform data from one representation to another, e.g. JSON to TypeScript.
- Document code: ask to generate a documentation based on code.
- 3rd party library: get information about a third party library that you want to use.
- Review accessibility: review html code for accessibility issues or make it compliant.
- Technology migration: switch from one implementation version to another, e.g. Java 20 to 21, RestTemplate to WebClient.
- Improve performance: ask about performance issues in the code and ways to improve.
- Understand: explain code.
- Port between languages: transform code from one language into another.
- Generate commit messages: generate git commit messages based on the changes.
- Overview: ask for a general overview of a project’s source code if it’s new to you to understand conventions, patterns and libraries used.
- Refactoring to patterns: if you see a code smell and the possibility to refactor to a design pattern, use the coding assistant.
- Generate performance tests: create performance tests, e.g. JMeter scripts, based on the APIs.
- Optimise internal exceptions and error: draft text for internal errors and exception for your fellow engineers and your future self.
- Generate API requests: create API requests in curl, postman, bruno or any other API tool.
- README: generate READMEs based on the code.
- Improvement and refactoring: ask for potential changes to your code.
These use cases are designed to help you unlock the full potential of GPT and coding assistants in your daily workflow. Whether you’re a seasoned software engineer or just starting out, these mini use cases will inspire you to find innovative solutions to common challenges.