[prompt-analysis] Copilot PR Prompt Analysis - November 18, 2025 #4273

2025-11-18T09:16:47Z

github-actions[bot]
bot Nov 18, 2025

🤖 Copilot PR Prompt Pattern Analysis - November 18, 2025

Summary

Over the last 30 days, Copilot generated 1,000 pull requests in the githubnext/gh-aw repository. Of the completed PRs, 77.0% were successfully merged, indicating strong overall performance. However, analysis reveals significant differences in success rates across prompt categories and characteristics.

The most successful prompts share common traits: they are concise (20-100 words), include specific file or code references, and focus on testing, updates, or bug fixes. Prompts that are overly verbose (>300 words), lack specificity, or attempt complex refactoring show lower merge rates.

Full Analysis Report

Overview Statistics

Analysis Period: Last 30 days
Total PRs: 1,000
Completed PRs: 999
Merged: 769 (77.0%)
Closed (not merged): 230 (23.0%)
Open: 1

Prompt Categories and Success Rates

Category	Total PRs	Merged	Closed	Success Rate
Testing	195	158	37	81.0% ✅
Update/Maintenance	496	393	103	79.2% ✅
Bug Fix	488	383	105	78.5% ✅
Documentation	226	177	49	78.3%
Workflow/Automation	480	369	111	76.9%
Feature Addition	454	348	106	76.7%
Refactoring	81	57	24	70.4% ⚠️
Uncategorized	83	56	27	67.5% ⚠️

Key Finding: Testing prompts have the highest success rate (81.0%), while refactoring and uncategorized prompts show the lowest rates (~70%).

Prompt Characteristics Analysis

Specificity Indicators

Characteristic	Merged PRs	Closed PRs	Impact
File references (e.g., `compiler.go`, `README.md`)	47.2%	37.6%	+9.7% higher merge rate ✅
Code references (e.g., function, class, method)	16.1%	9.3%	+6.8% higher merge rate ✅
URLs/links (commit refs, issue links)	48.5%	43.9%	+4.6% higher merge rate
Numbers (versions, PR numbers)	52.8%	50.7%	Minimal impact

Key Finding: Prompts with specific file or code references have significantly higher merge rates. Being specific pays off!

Prompt Length Analysis

Merged PRs: Average of 145 words
Closed PRs: Average of 150 words
Optimal Range: 20-100 words (concise but complete)

Key Finding: Overly verbose prompts (>300 words) correlate with lower success rates. The most successful prompts in our sample were exactly 20 words.

✅ Successful Prompt Patterns

Common Characteristics in Merged PRs

Concise and direct - Average 145 words, optimal 20-100 words
Action-oriented - Start with clear verbs: "Update", "Add", "Fix", "Generate"
Specific references - Mention exact files, functions, or commit hashes
Context provided - Include links to relevant issues, commits, or documentation
Single, clear objective - Focus on one task rather than multiple goals

Top Action Verbs in Successful PRs

update (655 uses) - Modifying existing code/docs
add (533 uses) - Adding new functionality
create (370 uses) - Creating new files/features
fix (201 uses) - Resolving bugs/issues
remove (162 uses) - Cleaning up code

Example Successful Prompts

1. PR #4247 - MERGED ✅ (20 words)

Update the agentic workflows copilot-* to use the shared agentic workflow to get copilot or data instead of custom steps

Why it succeeded: Concise, specific target (copilot-* workflows), clear action (update), specific change (use shared workflow)

2. PR #4239 - MERGED ✅ (20 words)

Update node.js version check (in makefile) to the default version available in action runners, v20.19.5. See the log in [GitHub link]

Why it succeeded: Specific file (Makefile), specific change (version number), includes evidence (log link)

3. PR #4129 - MERGED ✅ (20 words)

Generate annotations such that workflow_run is ignored by zizmor. The compiler already adds specific validation (role, fork) to secure it.

Why it succeeded: Technical specificity (workflow_run, zizmor), context provided (existing validation), clear goal

❌ Unsuccessful Prompt Patterns

Common Characteristics in Closed PRs

Overly verbose - Some closed PRs had 500+ word prompts
Vague objectives - "Improve" or "enhance" without specifics
Multiple tasks - Trying to accomplish too much in one PR
Lack of file references - Generic descriptions without specific targets
Complex refactoring - Large-scale changes without clear scope

Example Unsuccessful Prompts

1. PR #2912 - CLOSED ❌ (630 words)

create a github agentic workflow that analyzes Copilot agent sessions and extracts insights

Objective

Create a recurring GitHub Agentic Workflow...

Why it failed: Extremely verbose (630 words), complex multi-part task, creating entirely new workflow rather than focused change

2. PR #3188 - CLOSED ❌ (603 words)

create a github agentic workflow that: generates release notes after a new release is published, compares the old and new binaries and schema, and c...

Why it failed: Multiple objectives (generate notes + compare binaries + compare schema), overly complex, too many moving parts

3. PR #3994 - CLOSED ❌ (594 words)

This section details on the original issue you should resolve
(issue_title)[q] Shorten Q workflow description to 2 paragraphs(/issue_title)...

Why it failed: Verbose boilerplate, actual task buried in template, unclear core objective

🎯 Key Insights

1. Specificity Drives Success

Prompts that reference specific files have a 9.7% higher merge rate than generic prompts. Including code references (functions, classes) adds another 6.8% advantage.

Recommendation: Always mention the exact file, function, or component you want modified.

2. Conciseness is Critical

The most successful prompts averaged 20 words. While 145 words is acceptable, prompts over 300 words show significantly lower success rates.

Recommendation: Aim for 20-100 words. Be direct and eliminate unnecessary context.

3. Testing and Updates Win

Prompts focused on testing (81% success) and updates (79% success) dramatically outperform refactoring tasks (70% success).

Recommendation: Frame prompts as tests or updates rather than complex refactors when possible.

4. One Task, One PR

Prompts attempting multiple objectives (e.g., "create workflow + compare binaries + generate notes") have lower success rates.

Recommendation: Break complex tasks into multiple, focused PRs.

5. Action Verbs Matter

Start with clear action verbs: "Update", "Fix", "Add", "Create". Avoid vague verbs like "improve" or "enhance" without specifics.

Recommendation: Use concrete action verbs that clearly describe the change.

📋 Recommendations for Writing Effective Prompts

✅ DO:

Be specific - Mention exact files, functions, or components
- ❌ "Update the workflow"
- ✅ "Update .github/workflows/build.yml to use Node 20"
Keep it concise - Aim for 20-100 words maximum
- ❌ 600-word essay with background and objectives
- ✅ "Fix null pointer error in validateInput() function in validator.go"
Include references - Link to commits, issues, or documentation
- ❌ "Fix the bug mentioned earlier"
- ✅ "Fix bug in Add source field to track workflow origin in frontmatter #1234: https://github.com/org/repo/issues/1234"
Use action verbs - Start with "Update", "Fix", "Add", "Create"
- ❌ "There should be better error handling"
- ✅ "Add error handling to HTTP request function"
Focus on testing/updates - These have highest success rates
- ✅ "Add unit tests for authentication module"
- ✅ "Update dependencies to latest stable versions"

❌ AVOID:

Verbose prompts - Over 300 words significantly reduces success
Multiple objectives - One task per PR
Vague language - "Improve", "enhance" without specifics
Complex refactoring - Break into smaller, focused changes
Missing context - Always explain what and where

📈 Prompt Template (High Success Pattern)

[Action Verb] [Specific Component/File] to [Specific Change]

Example: Update src/compiler.go to support "all: read" permission syntax

Optional: [Link to issue/commit/documentation]

Characteristics:

✅ 20-100 words
✅ Specific file/component reference
✅ Clear action verb (Update, Fix, Add)
✅ Single, focused objective
✅ Optional context link

🔍 Additional Analysis

Most Common Keywords in Successful Prompts

issue (675 mentions) - Linking to specific issues
workflow (549 mentions) - Workflow-related changes
update (458 mentions) - Modification tasks
files (452 mentions) - File-specific changes

Success Rate by Prompt Element

Prompts with GitHub issue links: Higher merge rate
Prompts with commit references: Higher merge rate
Prompts with file paths: +9.7% merge rate
Prompts with function names: +6.8% merge rate

💡 Quick Takeaways

Be specific: Reference exact files/functions (+9.7% success)
Be concise: 20-100 words is optimal (vs 300+ words)
Focus on testing/updates: 81% success vs 70% for refactoring
One task per PR: Multi-objective prompts fail more often
Use action verbs: "Update", "Fix", "Add" perform best

Analysis based on 1,000 Copilot-generated PRs from the last 30 days in githubnext/gh-aw

AI generated by Copilot PR Prompt Pattern Analysis

2025-11-28T22:47:43Z

github-actions[bot]
bot Nov 28, 2025
Author

This discussion was automatically closed because it was created by an agentic workflow more than 1 week ago.

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