Automatically Improve Cursor Rules Using Custom Prompts

John Lindquist
Instructor

John Lindquist

Writing clear and effective prompts or rules for AI (.mdc files in Cursor) can be tricky. Your instructions might start out clumsy or ambiguous, leading to less-than-ideal AI responses. This lesson demonstrates how to quickly refine them by referencing a dedicated "best practices" rule file directly within Cursor's inline editor (Cmd+K).

Workflow demonstrated in this lesson:

  • Select the text (a prompt or section of a rule) you want to improve.
  • Use Cmd+K to open the inline AI editor.
  • Reference your separate prompt best practices rule file using @ (e.g., @prompt-improve.mdc).
  • Cursor's AI rewrites the selected text, applying the referenced best practices.
  • Accept the improved version (Cmd+Enter).
  • (Bonus) Use Cmd+L to send the edited section to the Agent chat and ask it to compare the changes with the previous version (using git history) and explain why the new version is better (e.g., improved clarity, stronger action verbs, explicit exclusions).

Key benefits:

  • Instantly improve prompt clarity and effectiveness based on your own defined standards stored in a rule file.
  • Reduces ambiguity in instructions, leading to better AI outcomes.
  • Leverages Cursor rules (.mdc files) as reusable templates for prompt refinement.
  • Provides insight into how and why prompts are improved via Agent comparison.

Stop wrestling with clunky prompts. Use this technique to systematically apply best practices and level up your AI instructions directly within Cursor.

# Improve the User's Prompt Following the Patterns Below

> Practical prompt patterns to help anyone get clearer, more reliable answers from an AI agent.

| # | Pattern                       | Why It Matters                                                          | Template                                                                 | Example Prompt                                                                                             |
|---|-------------------------------|-------------------------------------------------------------------------|--------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|
| 1 | Lead with the ask             | The model reads top-down; putting the goal first stops it wandering.    | `Do X. [context]`                                                        | "Summarize this PDF in 5 bullet points. The text is below: ..."                                            |
| 2 | Repeat the key ask at the end | Long contexts sometimes truncate; an end-cap protects you.              | `...[detail]... REMEMBER: Do X.`                                         | "List pros & cons, keep it balanced. REMEMBER: 5 pros, 5 cons."                                             |
| 3 | Specify output shape          | Dictating format cuts revision loops.                                   | `Return as: 1) short title, 2) table (CSV).`                             | "Give 3 holiday ideas. Format: destination, flight-time-hrs, avg cost."                                    |
| 4 | Use clear delimiters          | Backticks/headings/XML keep sections from blending.                     | `TEXT TO ANALYSE`                                                        | "Rate the style of the text between the fences."                                                            |
| 5 | Induce step-by-step thinking  | Planning first boosts multi-step accuracy.                              | `Think step-by-step then answer.`                                        | "Solve this puzzle. Think step-by-step before giving the final move."                                      |
| 6 | Ask it to plan its workflow   | For big jobs, AI outlines tasks before doing them.                      | `First draft a plan, wait, then execute.`                                | "We're writing an e-book. ❶ Outline chapters. ❷ Wait. ❸ When I say 'go', draft chapter 1."              |
| 7 | Limit or widen knowledge sources | Controls hallucination.                                               | `Use only the info below. / Combine basic knowledge + this context.`     | "Using only the product sheet below, write FAQs."                                                          |
| 8 | Guide information retrieval   | Helps AI pick the right docs before answering.                          | `List which docs look relevant, then answer.`                            | "30 sales memos attached. 1) Name the 5 most relevant. 2) Summarise their common points."                 |
| 9 | Show a style/example          | Anchors tone, length, vocabulary.                                       | `Match the style of: <example>`                                          | "Review this gadget in the style of the sample below: 'Short, witty, 3 key facts...'"                      |
| 10| Set correction handles        | One-line fixes let you steer quickly.                                   | `If length > 150 words, shorten.`                                        | "Describe blockchain to a child. If your answer is >150 words, cut it in half."                            |
| 11| Tell it when to stop or loop  | Prevents half-finished lists or runaway essays.                         | `Keep going until you list 20 ideas, then stop.`                         | "Brainstorm webinar titles. Give exactly 12, then finish."                                                  |
| 12| Request the hidden reasoning  | Good for audits; otherwise omit to keep it short.                       | `After the answer, include a brief reasoning section.`                   | "Which of these stocks looks over-valued? Answer first; add a 2-sentence rationale below a divider."        |
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Transcript

[00:00] I have a cursor rule which I'm working on right here that can help guide the AI to create a pull request. Now I feel like some of this language is clumsy and not as precise as it could be, so I'm going to select what I want to update. I'll hit command K and I'm going to reference another rule I have with at and type prompt and this is my prompt improve rule. Now once I reference this, this has the best practices for standard prompt engineering, which will essentially ask it to apply onto our current prompt. So once I hit enter here, this will rewrite my prompt.

[00:34] Let me hit command enter to accept this, scroll back up. This will rewrite this prompt, and to better understand this I'll bring this into our agent with command L. I'm gonna say please compare what we've edited in this file to our current commit and explain exactly how the language changed and why you think it changed. Then we'll just let this run. It will go ahead and run a comparison between the two sections and if you look at these changes, clarity, action verbs, explicit exclusions, and what exactly it did.

[01:04] So again I'll include this prompt improve prompt below, which as you're working on rules or any other prompts, can just take whatever you have, Command K, at prompt improve, hit enter, hit enter again, hit Command Enter to accept, and look at the changes, and automatically take your prompts to the next level.