system-prompts-and-models-of-ai-tools/EVOLUTION.md

🔄 Evolution of AI Coding Tool Prompts

Tracking how system prompts have changed from 2023 to 2025

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📊 Overview

This document tracks the evolution of AI coding assistant prompts by analyzing version-dated files, comparing early vs. late implementations, and identifying emerging trends.

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🕐 Timeline of Major Changes

Early 2023: Foundation Era

Characteristics:

• Simple, verbose instructions
• Basic tool sets (read, write, search)
• General-purpose guidance
• Limited context awareness
• No sub-agents

Example Tools from This Era:

• Early GitHub Copilot
• Basic Cursor iterations

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Mid 2023-Early 2024: Refinement Era

Characteristics:

• More structured instructions
• Tool categorization
• Better error handling
• Introduction of specialized modes
• First context management attempts

Example Tools:

• Cursor v1.0
• Early Claude Code experiments

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Mid 2024: Agent Era Begins

Characteristics:

• Sub-agent introduction
• Task delegation patterns
• Memory systems emerge
• Parallel execution emphasis
• Conciseness mandates

Example Tools:

• Cursor v1.2
• Amp early versions
• Claude Code

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Late 2024-2025: Sophisticated Era

Characteristics:

• Advanced agent architectures
• Reasoning model integration (o3)
• TODO/progress tracking
• AGENTS.md pattern
• MCP protocol support
• Extreme optimization focus

Example Tools:

• Amp (latest)
• Windsurf Wave 11
• Cursor Agent Mode
• GPT-5 integrations

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🔍 Key Evolution Patterns

1. Prompt Length: From Verbose to Concise

2023:

You should provide detailed explanations of what you're doing
and why. Make sure to explain your reasoning thoroughly so
the user understands your thought process. After completing
a task, summarize what you did and explain the changes made.

2025:

Be concise. Answer in 1-3 sentences. No preamble or postamble.
After editing, just stop.

Trend: 📉 Verbose → 📈 Terse Driver: Token costs + user preference for speed

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2. Tool Architecture: Simple to Complex

Early (2023):

Tools:
  - read_file
  - write_file
  - search
  - run_command

Modern (2025):

Tools:
  File Ops: read, write, edit, delete, format, glob
  Search: semantic, grep, file_search, codebase_search_agent
  Execution: bash, with background support
  Analysis: diagnostics, linting, typechecking
  Web: search, read_web_page
  Git: via bash with workflows
  Sub-agents: Task, Oracle, specialized agents
  Context: memory, AGENTS.md
  TODO: todo_read, todo_write
  MCP: read_mcp_resource (emerging)

Trend: 5 tools → 20+ tools + sub-agents Driver: More capabilities, better organization

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3. Code Comments: Encouraged to Forbidden

2023:

Add helpful comments to explain your code changes.
Document what you're doing for future maintainers.

2025:

IMPORTANT: DO NOT ADD ANY COMMENTS unless asked.
Only add comments when:
1. User explicitly requests them
2. Code is complex and requires context

Trend: Comments encouraged → Comments forbidden Driver: AI explanations belong in chat, not code

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4. Execution Strategy: Serial to Parallel

2023:

Use the read_file tool to read the file.
Then use the search tool to find relevant code.
Then use the write_file tool to make changes.

2025:

Default to PARALLEL for all independent work.
Call multiple tools simultaneously. Serialize only
when there is a strict dependency.

Trend: Sequential → Parallel by default Driver: Performance optimization (3-10x faster)

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5. Context Management: Ad-hoc to Structured

Early Approach:

Try to remember what the user has told you.
Keep track of the conversation context.

Modern Approach:

System 1: AGENTS.md file pattern
- Commands, style, structure
- Auto-loaded each session
- User-maintained

System 2: Explicit memory tools
- Memory rating
- Persistent storage
- Strategic recall

System 3: Prompt caching
- Cache system prompts
- Cache common context
- Reduce costs

Trend: Implicit → Explicit + Persistent Driver: Context limits + cost optimization

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6. Agent Architecture: Monolithic to Modular

Early (2023):

Single AI agent handles everything

Mid (2024):

Main agent + occasional sub-agent for specific tasks

Modern (2025):

Agent Orchestra:
├── Main Agent (coordination)
├── Task Executors (implementation)
├── Search Agents (discovery)
├── Oracle/Reasoning Agent (planning)
└── Specialized Agents (domain-specific)

Trend: Monolithic → Multi-agent systems Driver: Specialization + parallel work

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7. User Communication: Verbose to Minimal

Version History Example (Cursor):

v1.0:

Provide helpful explanations along the way.

v1.2:

Keep your answers short and impersonal.

Agent Prompt (latest):

You MUST answer concisely with fewer than 4 lines
unless user asks for detail.

CLI Prompt 2025-08-07:

IMPORTANT: Keep your responses short. You MUST answer
concisely with fewer than 4 lines (not including tool
use or code generation).

Trend: Explanatory → Minimal Driver: Efficiency + user feedback

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8. Error Handling: Permissive to Strict

2023:

If you encounter an error, try to work around it.
Keep trying different approaches.

2025:

If same error persists:
- Stop after 3 attempts
- Try alternative approach OR ask user
- NEVER loop indefinitely

Trend: Keep trying → Fail fast + ask Driver: User frustration with loops

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9. Security: Basic to Comprehensive

2023:

Don't include passwords in code.

2025:

NEVER:
- Log API keys, tokens, passwords
- Expose secrets in errors
- Commit secrets to git
- Create malicious code

Defensive security only:
- Security analysis allowed
- Vulnerability explanations allowed
- Exploits FORBIDDEN

Trend: Simple rules → Comprehensive guardrails Driver: Security incidents + enterprise needs

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10. Git Integration: Basic to Workflow-Aware

Early:

You can use git commands if needed.

Modern:

Standard git workflow:
1. Check status + diff in parallel
2. Analyze changes + check for secrets
3. Stage relevant files
4. Commit with formatted message
5. Include attribution footer
6. Verify commit succeeded

NEVER:
- Use interactive git (-i flag)
- Update git config
- Push without permission

Trend: Basic commands → Structured workflows Driver: Quality control + safety

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📈 Emerging Patterns (2024-2025)

Pattern 1: TODO/Progress Tracking

Status: New in late 2024

Implementation:

Tools: todo_write, todo_read

Usage:
- Create at task start
- Mark in-progress before starting
- Complete immediately after finishing
- Give user visibility

Tools Adopting: Claude Code, Amp, Windsurf Driver: User need for transparency

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Pattern 2: Reasoning Model Integration

Status: Emerging 2025

Pattern (Amp Oracle):

Oracle Tool:
- Powered by OpenAI o3 reasoning model
- For planning, debugging, architecture
- Returns detailed analysis
- Separate from execution agent

Trend: Separation of thinking vs. doing Future: More specialized reasoning agents

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Pattern 3: AGENTS.md Context Files

Status: Emerging standard

Content:

AGENTS.md contains:
- Common commands (test, build, lint)
- Code style preferences
- Project structure notes
- Tool-specific instructions

Tools Adopting: Claude Code, Amp, spreading Driver: Persistent, user-controllable context

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Pattern 4: MCP (Model Context Protocol)

Status: Very new, gaining traction

Purpose: Standardized tool/resource interface

Example:

read_mcp_resource:
  server: "filesystem-server"
  uri: "file:///path/to/resource"

Trend: Moving toward interoperable tool ecosystem

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🔄 Version-Specific Changes

Cursor Evolution:

v1.0 → v1.2:

• Added agent mode
• Introduced memory system
• More concise instructions
• Better tool organization

v1.2 → Agent Prompt:

• Separated agent vs. chat modes
• CLI variant added
• More explicit parallelism
• Stricter conciseness rules

Agent → Agent CLI 2025-08-07:

• Terminal-optimized
• Even terser responses
• More structured tool use

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GitHub Copilot Evolution:

Progression visible through model files:

GPT-4.1.txt → GPT-5.txt:

• More sophisticated instructions
• Better context handling
• Advanced model capabilities

Addition of claude-sonnet-4.txt, gemini-2.5-pro.txt:

• Multi-model support
• Model-specific optimizations
• Capability-aware instructions

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Windsurf Evolution:

Wave progression: (implied by "Wave 11")

• Suggests iterative improvements
• Each wave = new architecture iteration
• Continuous refinement process

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📊 Quantitative Changes

Instruction Length:

• 2023 Average: 5,000-10,000 tokens
• 2025 Average: 15,000-30,000 tokens
• Trend: More comprehensive, better structured

Tool Count:

• 2023: 5-10 tools
• 2025: 15-25 tools + sub-agents
• Trend: More specialized, better organized

Example Count:

• 2023: 2-5 examples
• 2025: 10-20 examples per major pattern
• Trend: More example-driven learning

Security Rules:

• 2023: 1-2 mentions
• 2025: Dedicated security section
• Trend: Comprehensive security focus

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🎯 Key Insights

1. Convergent Evolution

Tools independently arrived at similar solutions:

• Conciseness mandates
• Parallel execution
• No code comments
• Security guardrails
• TODO tracking

Insight: These are genuinely good patterns, not just copying.

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2. Token Economics Drive Design

Most changes reduce token usage:

• Shorter responses
• Parallel operations
• Prompt caching
• No verbose explanations

Insight: Economics shape UX.

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3. User Feedback Loop

Clear pattern of refinement based on usage:

• Comments → No comments (users found them annoying)
• Verbose → Concise (users wanted speed)
• Serial → Parallel (users needed performance)

Insight: Real-world use drives evolution.

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4. Specialization Over Generalization

Trend toward:

• Sub-agents for specific tasks
• Specialized models (reasoning vs. execution)
• Domain-specific tools

Insight: One-size-fits-all doesn't work.

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5. Transparency Matters

Recent additions focus on visibility:

• TODO lists
• Progress tracking
• Explicit plans

Insight: Users want to see what's happening.

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🔮 Future Predictions

Based on observed trends:

Short Term (2025):

1. MCP adoption will grow
2. Reasoning models become standard
3. More sub-agents for specialization
4. Better context management (beyond AGENTS.md)
5. Tighter IDE integration

Medium Term (2026-2027):

1. Autonomous agents become mainstream
2. Multi-agent collaboration improves
3. Real-time collaboration features
4. AI pair programming workflows
5. Custom model fine-tuning

Long Term (2028+):

1. AGI-level coding assistants
2. Full project autonomy
3. Natural language as primary interface
4. AI-to-AI communication protocols
5. Seamless human-AI team integration

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📚 Lessons Learned

What Works:

✅ Concise communication ✅ Parallel execution by default ✅ Clear tool separation ✅ Example-driven instructions ✅ User control and visibility ✅ Security by default ✅ Modular architecture

What Doesn't Work:

❌ Verbose explanations ❌ Serial execution ❌ Monolithic agents ❌ Implicit context management ❌ Looping on errors ❌ Ignoring user preferences

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🎯 Conclusion

The evolution from 2023 to 2025 shows clear trends:

• Simpler for users (fewer words, clearer actions)
• More complex under the hood (more tools, agents)
• Faster (parallel execution, optimization)
• Safer (comprehensive security)
• More transparent (TODO lists, progress tracking)

The future likely continues these trends with increased autonomy, better specialization, and seamless integration.

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This analysis is based on version-dated files and comparative analysis of 31+ tools in this repository.

Last Updated: October 2, 2025