Custom GPT Conversation Starters: Useful Work Examples

Conversation starters are the suggested prompts that appear on your custom GPT's opening screen. A good starter tells the user what input the assistant needs and what kind of output to expect, making the first useful exchange take seconds instead of minutes.

Saving your approved starter patterns in a tool like fenbs means you can reuse them in Claude, Cursor, and other connected assistants without rebuilding them for each platform.

What makes a good conversation starter

A starter that says "Help me write something" is too vague to be useful. A good starter requests the specific input the assistant needs and signals what output the user will get. It should be short enough to read quickly and specific enough to guide the first message.

The GPT builder allows up to four starters on the opening screen. Choose the four inputs users send most often, not the four tasks you think are most interesting. Starters that match real usage patterns get clicked; starters that cover edge cases sit unused.

The best starters are written in the first person from the user's point of view ("Review this contract clause for plain-English risk points") or as direct requests that include a placeholder for the input ("Summarize this meeting transcript into decisions and action items: [paste transcript]"). Both patterns make the expected input obvious.

Starters for writing and editing tasks

These starters work well for GPTs that edit documents, rewrite content, or generate drafts:

• "Rewrite this paragraph in our house style: [paste paragraph]"
• "Turn these bullet points into a two-paragraph email ready to send: [paste bullets]"
• "Review this proposal introduction for clarity and flag any sentences over twenty-five words: [paste intro]"
• "Generate three alternative subject lines for this email. Keep each under eight words: [paste email]"
• "Check this job posting for language that may discourage qualified applicants: [paste posting]"

Starters for analysis and research

These starters work for GPTs that summarize documents, extract information, or compare options:

• "Summarize this research paper in five bullet points covering claim, method, findings, limitations, and relevance: [paste abstract or paper]"
• "List the obligations and deadlines in this contract, organized by party: [paste contract text]"
• "Compare these two options on the criteria I care about: [describe options and criteria]"
• "Extract every action item from these meeting notes and list them with owners and deadlines: [paste notes]"
• "Identify the three strongest and three weakest points in this argument: [paste text]"

Starters for planning and review

These starters work for GPTs that help with project planning, review checklists, or decision support:

• "Turn this project goal into a five-step plan with one deliverable per step: [describe goal]"
• "Review this plan for gaps: steps with no owner, no deadline, or no success criterion: [paste plan]"
• "List the risks in this approach and a mitigation for each: [describe approach]"
• "Break this large task into sub-tasks that each take two hours or less: [describe task]"
• "What am I missing in this checklist for [type of event or project]?"

Starters for technical tasks

These starters work for GPTs that help with code review, documentation, or technical explanation:

• "Explain what this code does in plain English, one sentence per function: [paste code]"
• "Write a pull request description for these changes using our PR template: [paste diff or summary]"
• "Suggest test cases for this function covering happy path, edge cases, and failure modes: [paste function signature]"
• "Rewrite this error message in plain English with the most likely cause and next steps: [paste error]"
• "Review this SQL query for correctness and performance issues: [paste query]"

Tips for writing your own starters

• Start with the output type, not the input: "Summarize..." "List..." "Review..." "Generate..." tells the user what they will get.
• Include a [placeholder] where the user should paste their content. It removes the guesswork.
• Limit each starter to one task. A starter that asks for a summary and a rewrite in the same message will produce inconsistent results.
• Write starters in the same voice as your instructions. Consistency between the starters and the instructions signals to users that the GPT is well-configured.
• Update starters after you see how users actually open conversations. The first messages real users send are better starters than the ones you invented.
• Test each starter yourself before publishing. Click it, see what the assistant produces, and check whether the output is close to what you promised. A starter that sets an expectation the GPT cannot meet will frustrate users on their first interaction.

How to do this with fenbs

1. Create a free account and add a project for the work this GPT supports.
2. Add each tested conversation starter as a Skill in fenbs with a note on when to use it.
3. When a different AI app takes over part of the workflow, it can read the same starters from fenbs via MCP.
4. After real users interact with the GPT, update the Skill to reflect the starters that actually work.
5. Add a Lessons learned entry if a starter consistently produces wrong outputs, so the next version starts from better evidence.

For guidance on building the GPT that uses these starters, see the custom GPT builder or how to build a custom GPT. To compare custom GPTs with other platforms, see Claude Projects vs. custom GPTs.