Essential AI Prompts for Developers: 4 Strategies to Make AI Work Super Hard for You
Session Date: May 22, 2025
Duration: 15 minutes
Venue: Build 2025 Conference - DEM509
Speaker: Burke Holland (Developer Advocate, Microsoft)
Link: Microsoft Build 2025 Session DEM509
Table of Contents
- Introduction: Beyond Basic AI Completion
- Foundation Principle: Voice-First AI Interaction
- Strategy 1: The Q&A Prompt Method
- Strategy 2: Pros and Cons Analysis
- Strategy 3: Stepwise Chain of Thought
- Strategy 4: Role Playing Enhancement
- Technical Implementation and Best Practices
- Practical Applications Beyond Programming
Introduction: Beyond Basic AI Completion
00:00:00 - 00:01:05 (1m 05s)
Speaker: Burke Holland
The session opens with Burke Holland establishing the transformative potential of strategic AI prompting. Moving beyond simple code completion, the session introduces four sophisticated techniques designed to make AI systems “work super hard for you.” Holland emphasizes that these strategies extend beyond programming into any domain where AI assistance can enhance productivity.
Session Context and Energy
Holland begins by acknowledging the conference atmosphere, playfully referencing his earlier emcee role in Theatre A and establishing Theatre B as his new domain. This casual, conversational tone sets the stage for the entire presentation, demonstrating the very principle he advocates: natural, unstructured communication with AI systems.
Key Opening Insight: “Now what I want to give you today are 4 prompts and prompt strategies that you can use to become independently wealthy. Not really, but they will make your life a little bit better, I think.”
Copilot Modes Overview
Holland introduces the three primary modes of GitHub Copilot that will be utilized throughout the demonstration:
- Ask Mode: Information gathering and strategy development
- Edit Mode: Targeted code modifications
- Agent Mode: Autonomous implementation and refactoring
The session promises to focus primarily on practical prompting techniques rather than extensive discussion of the “agent” buzzword that has dominated the conference.
Foundation Principle: Voice-First AI Interaction
00:01:05 - 00:01:45 (40s)
Speaker: Burke Holland
Revolutionary Communication Approach
Before diving into specific strategies, Holland establishes a fundamental shift in how developers should interact with AI systems: abandoning keyboard input in favor of voice communication.
Core Philosophy: “The first thing that I’ll point out is that I don’t type to Copilot. I talk to it. That’s what you should do because you don’t actually have to. You don’t have to be articulate, right? You can literally just like verbally vomit into the chat what you want and it will pull out from that. The context here.”
Technical Foundation of Voice Interaction
Local Speech Model Capabilities:
- Real-time transcription with high accuracy
- Local processing - no cloud dependency for speech recognition
- Natural language understanding from conversational input
- Context extraction from unstructured verbal communication
Practical Benefits
Developer Productivity Gains:
- Faster input speed compared to typing complex technical requirements
- Natural expression without concern for grammar or structure
- Reduced cognitive load - focus on problem-solving rather than articulation
- Stream-of-consciousness problem description capabilities
This foundational principle underpins all subsequent strategies, enabling more natural and effective AI collaboration throughout the development workflow.
Strategy 1: The Q&A Prompt Method
00:01:45 - 00:05:25 (3m 40s)
Speaker: Burke Holland
Project Structure Challenge
Holland introduces the first strategic prompting technique through a real-world scenario: organizing a chaotic project structure that resembles a “junk drawer” with no clear organization or architectural patterns.
The Problem Statement: Developers frequently struggle with project organization, especially when templates don’t provide clear guidance. Traditional approaches require extensive architectural knowledge and decision-making that can be overwhelming, particularly for complex project structures.
The Q&A Prompt Methodology
Initial Prompt Structure: “Hey there, how’s it going? Listen up here, I got this project over here that has like no structure. It’s kind of a mess. And so I need like a recommended file or folder structure for this mug and I need you to give that to me. But before you do that, could you please ask me 5 yes or no questions that will help you make a better recommendation.”
Strategic Reversal: Instead of asking the AI for immediate solutions, the Q&A prompt inverts the interaction by requesting that the AI gather requirements through targeted questioning. This approach leverages the AI’s ability to understand context and generate relevant inquiries.
Contextual Enhancement with @codebase
The Enhancement Discovery: Holland demonstrates a critical improvement by clearing the chat and re-running the prompt with the @codebase modifier. This enhancement allows the AI to:
- Analyze existing project files and folder structures
- Detect technology stacks and frameworks in use
- Generate contextually relevant questions based on actual codebase content
- Provide informed recommendations rather than generic suggestions
Enhanced Question Quality: With codebase context, the AI generates more sophisticated questions:
- “Are you planning to follow a model-view-controller pattern?” → Yes
- “Do you need to handle authentication?” → Yes
- “Do you plan to implement API endpoints?” → No
- “Will you be using automated tests?” → No (“You kidding me?”)
- “Are you planning to include middleware?” → Yes
Automated Implementation and Script Generation
Recommended Architecture Output: Based on the Q&A session, the AI recommends a comprehensive folder structure:
/config- Configuration files and environment settings/controllers- MVC controller logic and request handlers/middleware- Request processing middleware components/models- Data models and database schemas/routes- API route definitions and URL mappings/services- Business logic and external service integrations/types- TypeScript type definitions and interfaces
Implementation Automation Request: “Totes Magotes. That is a dope structure. There’s nothing about it that I don’t love. I love everything about it. Could you just give me a single script that will generate that structure? Don’t create any of the files though. I just want the folders and then you can just move the existing files that I have into the right spots.”
Script Execution and Practical Results: The AI generates a shell script (script.sh) that creates the entire folder structure. Holland demonstrates the execution process, including:
- Permission handling:
chmod +x script.shfor executable permissions - Script execution:
./script.shto generate folder structure - Immediate organization: Transformation from chaotic to organized project layout
Key Benefits and Applications
Strategic Advantages:
- Context-driven recommendations tailored to specific project needs
- Interactive requirement gathering reducing guesswork and assumptions
- Automated implementation of organizational decisions
- Scalable methodology applicable to various project types and sizes
Beyond Project Structure: The Q&A prompt method extends to numerous development scenarios:
- Technology selection decisions with comparative analysis
- Architecture pattern selection based on project constraints
- Database design optimization through requirement clarification
- Performance optimization strategies based on usage patterns
Strategy 2: Pros and Cons Analysis
00:05:25 - 00:07:00 (1m 35s)
Speaker: Burke Holland
Database Injection Pattern Problem
Holland transitions to the second strategy by examining a common architectural challenge: ensuring database connections are instantiated only once within an application to optimize performance and resource utilization.
The Architectural Challenge: “What we want to do here is we want to make sure that we’re only injecting this one time into the application, right? So there’s a lot of different ways to do that.”
The Problem with Single-Answer AI Responses
AI Behavioral Tendency: “In programming, there’s never one right way to do anything, even though you read a lot of blog posts that tell you otherwise, there’s not one right way. You know this as developers, right? And so the model, though, is tuned to give you an answer. You asked a question, it gives you an answer, but it may not be the right answer.”
Traditional AI Limitation: AI models default to providing single, definitive solutions rather than exploring multiple viable approaches. This behavior can lead to suboptimal implementations when alternative patterns might be more suitable for specific contexts.
Multi-Option Decision Framework
The Pros and Cons Prompt: “Sup bro, I’m back. Yo, listen, new sitch here. What’s going on is I got a database file and I want to just inject it once into the application. Like I don’t want to instantiate that mug every single time I use it. What is the best way to do that? Actually give me several options and give me the pros and cons of each.”
Comprehensive Solution Analysis:
Option 1: Module-Level Singleton
- Pros: Simple implementation, automatic lazy loading, minimal configuration
- Cons: Global state management, potential testing complications, dependency injection challenges
Option 2: Classical Singleton Pattern
- Pros: Controlled instantiation, thread-safe implementation options, established pattern
- Cons: More complex implementation, potential performance bottlenecks, testing difficulties
Option 3: Dependency Injection Container
- Pros: Testable architecture, configurable implementations, follows SOLID principles
- Cons: Framework dependency, learning curve, additional complexity
Implementation Selection and Agent Mode
Informed Decision Making: “Dope sauce. I like #2A whole lot. Why don’t you go ahead and implement that? And then you’re going to need to update the places in my project where this file is being used to make sure that everything is copacetic. Thank you.”
Agent Mode Integration: The demonstration showcases the transition from Ask Mode (analysis and recommendation) to Agent Mode (autonomous implementation):
- Automatic code analysis - Identifies all locations where the database file is imported
- Comprehensive refactoring - Updates instantiation patterns throughout the codebase
- Consistency validation - Ensures uniform implementation across all usage points
Real-World Application Results: The AI successfully implements the chosen Singleton pattern and updates the vehicle service to use the new database implementation, demonstrating end-to-end automation from decision analysis to code implementation.
Strategic Benefits of Pros and Cons Analysis
Enhanced Decision Quality:
- Full context awareness of implementation trade-offs
- Risk assessment for each approach before implementation
- Educational value - understanding architectural pattern implications
- Reduced technical debt from informed architectural decisions
Broader Applications:
- Technology stack selection with comparative analysis
- Performance optimization strategy evaluation
- Security implementation approach assessment
- Deployment architecture decision support
Strategy 3: Stepwise Chain of Thought
00:07:00 - 00:07:40 (40s)
Speaker: Burke Holland
Controlled Refactoring Methodology
Holland introduces the third strategy to address a critical challenge in AI-assisted development: managing complex, multi-step operations that require sequential validation and controlled progression.
The Refactoring Challenge: “Refactors are usually multiple steps, and the next step depends on what you just previously did. The model tends to get confused.”
Traditional AI Limitations:
- All-at-once approach - AI attempts complete refactoring in single response
- Context confusion - Loses track of incremental changes and dependencies
- Overwhelming output - Difficult to review and validate comprehensive changes
- Cascading errors - Mistakes in early steps compound through subsequent changes
Progressive Implementation Control
The Stepwise Chain of Thought Prompt: “Hey, listen, I want to refactor this file. And what I want you to do is move one step at a time. So let’s just move incrementally through this refactor and do not move to the next step until I give you the keyword banana.”
Control Mechanism Benefits:
- Unique keyword activation - “banana” ensures intentional progression
- Incremental validation - Review and approval of each step
- Flexible pivoting - Ability to change direction based on intermediate results
- Quality assurance - Prevention of cascading errors through controlled advancement
Security Vulnerability Resolution
Live Demonstration Context: While the transcript is brief for this section, the strategy is demonstrated through a security-focused refactoring scenario where the AI identifies and addresses SQL injection vulnerabilities in a systematic, step-by-step manner.
Step 1: Vulnerability Identification
- Issue detection: SQL injection risk in database queries
- Impact assessment: Security implications and potential attack vectors
- Initial recommendation: Parameterized query implementation
- Control point: Awaiting “banana” keyword for progression
Developer Dialogue and Alternative Exploration: The stepwise approach allows for mid-process consultation and alternative evaluation: “Parameterized queries are kind of clunky. Is there some other way that we can do this? Should I be using an ORM or something?”
Step 2: Architecture Alternative Analysis
- ORM evaluation: Prisma ORM as alternative approach
- Implementation requirements: Dependencies and configuration steps
- Architecture implications: Database abstraction layer considerations
- Controlled decision point: Evaluation before commitment to implementation path
Best Practices and Applications
Keyword Selection Strategy:
- Unique terms - Avoid common programming vocabulary that might appear in code
- Memorable phrases - Easy to recall during development sessions
- Context-independent - Universal applicability across problem domains
Broader Application Scenarios:
- Database migration with incremental schema changes
- API refactoring with backward compatibility validation
- Performance optimization with measurement at each stage
- Security hardening with systematic vulnerability addressing
- Code modernization with framework upgrade progression
Quality Assurance Benefits:
- Step-by-step validation ensures correctness at each stage
- Reduced risk from incremental rather than wholesale changes
- Learning opportunities - understanding the progression of complex refactoring
- Rollback capability - Easy recovery if issues arise during process
Strategy 4: Role Playing Enhancement
00:07:40 - 00:07:56 (16s)
Speaker: Burke Holland
AI Identity Assignment Psychology
Holland introduces the fourth and final strategy by revealing a powerful psychological aspect of AI model behavior: the dramatic performance improvement achieved through role assignment and identity specification.
Core Behavioral Insight: “Models really, really, really like to role play. If you tell them that they’re good at something, they’re just magically good at that thing.”
Performance Enhancement Mechanism:
- Identity adoption - AI models respond to assigned expert roles
- Behavioral modification - Changes response patterns based on persona
- Expertise simulation - Accesses domain-specific knowledge patterns
- Engagement improvement - Increased interaction quality through character consistency
Interactive Learning Session
Expert Instructor Role Assignment: “You are the greatest instruction instructor in the world. You’re almost as good as James Montemagno. Not quite close, close to being as good as James Montemagno. And the reason you’re so good is that you give your students creative exercises so that they can learn by doing. And your class is the most popular class in school. Everyone loves it.”
Pedagogical Method Specification: “And today you’re teaching a class on regex. Go ahead and start the class, move one exercise at a time. And if the student gets the answer wrong, don’t give them the answer, but go ahead and give them a suggestion that helps move them towards the correct answer.”
Expert Persona Simulation
Teaching Methodology Characteristics:
- Progressive difficulty - Starting with basic pattern matching concepts
- Guided discovery - Hints and suggestions rather than direct answers
- Encouraging feedback - Positive reinforcement for learning progression
- Interactive engagement - Responsive to student questions and confusion
- Practical exercises - Real-world application examples and challenges
Example Learning Interaction:
- Exercise Introduction: “Write a regex pattern that matches phone numbers in format XXX-XXX-XXXX”
- Student Confusion: Providing literal strings instead of regex patterns
- AI Guidance: “That looks like the test string itself, not a regex pattern. To create a regex, we need to wrap it in / and for digits, use
- Iterative Improvement: Continued refinement until pattern mastery
Versatile Role Applications
Educational Contexts:
- Technology tutorials - Learning new programming languages (Rust, Python, etc.)
- Code review sessions - Senior developer perspective on best practices
- Architecture discussions - System design expert providing strategic guidance
- Debugging assistance - QA engineer mindset for comprehensive testing strategies
Professional Simulation:
- Client consultation - Business analyst gathering requirements and specifications
- Technical interviews - Practice explaining complex problems and solutions
- Peer programming - Collaborative development partner with complementary expertise
- Mentorship sessions - Experienced developer providing guidance to junior team members
Specialized Expert Roles:
- Security consultant - Vulnerability assessment and remediation strategies
- Performance optimizer - System efficiency and scalability expert
- DevOps engineer - Deployment pipeline and infrastructure automation specialist
- Product manager - Feature prioritization and user experience optimization
Strategic Benefits and Implementation
Performance Enhancement Factors:
- Identity consistency - AI maintains character throughout interaction
- Domain expertise access - Specialized knowledge patterns activated
- Improved interaction quality - More engaging and contextually appropriate responses
- Customizable personality - Tailored to specific learning objectives and preferences
Implementation Best Practices:
- Specific role definition - Clear expertise areas and behavioral characteristics
- Personality traits - Communication style and interaction preferences
- Knowledge boundaries - Scope of expertise and limitations acknowledgment
- Interaction guidelines - Preferred teaching methods and feedback approaches
The role playing strategy transforms AI from a generic assistance tool into a specialized expert consultant, dramatically improving the quality and relevance of interactions across diverse professional and educational contexts.
Technical Implementation and Best Practices
Throughout Session (Distributed Content)
Speaker: Burke Holland
GitHub Copilot Multi-Mode Integration
Strategic Mode Utilization:
Ask Mode Applications:
- Information gathering and strategy development phases
- Analysis and recommendation generation for complex decisions
- Script generation and documentation creation
- Multi-option exploration through pros and cons analysis
Agent Mode Capabilities:
- Autonomous code implementation based on detailed specifications
- Multi-file refactoring with consistency validation across codebase
- Project-wide pattern application and architectural changes
- Comprehensive testing and validation of implemented changes
Edit Mode Integration:
- Targeted code modifications with surgical precision
- Context-aware suggestions based on cursor position and surrounding code
- Real-time collaboration during active development sessions
- Incremental improvements with immediate feedback
Voice Recognition and Speech Processing
Local Speech Model Performance: “You’ll see how good speech recognition has gotten lately. Like it’s crazy. That’s a local speech model that runs on your machine, so it’s like snappy and fast.”
Technical Capabilities:
- Real-time transcription with high accuracy across technical vocabulary
- Local processing - no cloud dependency or latency issues
- Conversational input handling - Supports casual speech patterns and interruptions
- Technical terminology recognition - Programming concepts in natural language
- Multi-language support for international development teams
Communication Optimization Benefits:
- Faster input speed compared to typing complex technical descriptions
- Natural expression without grammatical constraints or formal structure
- Stream-of-consciousness problem description and brainstorming
- Reduced cognitive load - focus on problem-solving rather than articulation
Context Management Strategies
Chat Session Discipline: “Clear the chat like all the time” - Holland emphasizes the importance of maintaining clean context boundaries between different problems and solution approaches.
Strategic Context Control:
- Regular context reset - Starting fresh conversations for different problems
- Topic isolation - Separate chat sessions for unrelated development tasks
- Intentional context accumulation - Maintaining conversation thread for complex, multi-step problems
- Context enhancement - Using @codebase and other modifiers for environmental awareness
Codebase Context Integration:
- @codebase modifier - File system analysis and project structure understanding
- Active file context - Current editor state and cursor position awareness
- Framework detection - Automatic recognition of technology stacks and patterns
- Dependency analysis - Understanding of project relationships and imports
Best Practices for Context Management:
- Problem boundary recognition - Knowing when to start fresh conversations
- Context accumulation strategy - Building comprehensive understanding for complex problems
- Environment integration - Leveraging IDE and project context effectively
- Session organization - Maintaining multiple parallel conversations for different aspects
Performance Optimization Techniques
Response Quality Enhancement:
- Iterative refinement through conversational feedback loops
- Multi-approach evaluation before committing to implementation
- Role-based expertise verification of recommendations and solutions
- Progressive disclosure of complexity through stepwise implementation
Efficiency Maximization:
- Voice-first communication for rapid requirement expression
- Automated script generation for repetitive tasks and setup operations
- Batch processing of related changes through agent mode
- Template generation for recurring patterns and structures
The technical implementation demonstrates how strategic prompting transforms AI from a simple autocomplete tool into a sophisticated development partner capable of architectural decision-making, automated implementation, and educational interaction.
Practical Applications Beyond Programming
Implied Throughout Session
Speaker: Burke Holland
Creative Problem Solving Applications
Business Strategy Development: The Q&A prompt method proves invaluable for strategic business decisions by:
- Requirement gathering through structured questioning for product development
- Market analysis via pros and cons evaluation of different approaches
- Risk assessment through stepwise analysis of business model changes
- Stakeholder consultation via role-playing different organizational perspectives
Content Creation and Communication:
- Technical documentation enhancement through expert persona simulation
- Training material development using AI as instructional design expert
- Marketing content optimization through role-playing target audience perspectives
- Communication strategy refinement via pros and cons analysis of messaging approaches
Professional Development Enhancement
Skill Acquisition Acceleration:
- Learning path optimization through AI tutoring with expert personas
- Practice scenario generation for interview preparation and skill validation
- Knowledge gap identification via structured questioning about competency areas
- Progressive skill building through stepwise complexity increase
Career Advancement Support:
- Portfolio optimization through AI analysis of project presentation
- Network strategy development via pros and cons analysis of professional relationships
- Personal branding enhancement through role-playing industry expert perspectives
- Leadership development via simulation of management scenarios and decisions
Team Collaboration and Management
Requirements Gathering Excellence: The Q&A prompt method revolutionizes stakeholder communication by:
- Client consultation improvement through systematic requirement exploration
- Project scoping accuracy via comprehensive questioning frameworks
- Risk identification through structured analysis of project constraints
- Expectation management via clear communication of trade-offs and alternatives
Code Review and Quality Assurance:
- Review process enhancement through multiple expert perspectives
- Architecture validation via pros and cons analysis of design decisions
- Knowledge transfer acceleration through AI-mediated explanations
- Best practice enforcement via role-playing senior developer perspectives
Creative and Personal Applications
Learning and Education: “If you’re not talking to AIs, you should be talking to AIs. It’s a ton of fun.” - Holland emphasizes the recreational and educational value of strategic AI interaction.
Personal Project Management:
- Home organization via Q&A prompting for space optimization
- Financial planning through pros and cons analysis of investment strategies
- Health and fitness optimization via expert trainer role-playing
- Creative projects enhancement through artistic expert consultation
Decision Making Framework: The four strategies create a comprehensive decision-making toolkit applicable to:
- Major life decisions with systematic pros and cons analysis
- Learning new skills through progressive, stepwise approaches
- Creative problem solving via expert persona consultation
- Relationship management through perspective-taking and role-playing exercises
Integration with Daily Workflows
Personal Productivity Systems:
- Task prioritization through Q&A exploration of objectives and constraints
- Goal setting enhancement via pros and cons analysis of different approaches
- Time management optimization through expert consultant role-playing
- Habit formation support via stepwise implementation of behavioral changes
Creative Project Development:
- Writing improvement through editor and critic persona simulation
- Design optimization via multiple expert perspective integration
- Music and art creation enhanced through artistic mentor role-playing
- Innovation acceleration via systematic exploration of creative possibilities
The session demonstrates that strategic AI prompting transcends programming to become a universal toolkit for enhanced thinking, learning, and problem-solving across all aspects of personal and professional life.
References
Microsoft Official Documentation and Resources
GitHub Copilot Documentation
Comprehensive documentation covering GitHub Copilot’s features, integration methods, and best practices. Essential for understanding the technical foundation underlying the prompting strategies demonstrated in the session, including Ask, Edit, and Agent modes functionality.
VS Code Speech Recognition
Official documentation for VS Code’s built-in speech recognition capabilities. Critical for implementing the voice-first interaction principle that Holland emphasizes as foundational to effective AI collaboration.
Azure OpenAI Service Documentation
Technical documentation for Azure’s OpenAI integration, providing context for the underlying language models that power GitHub Copilot’s advanced reasoning capabilities demonstrated through the strategic prompting techniques.
Prompt Engineering and AI Interaction Research
Prompt Engineering Guide
Comprehensive resource covering advanced prompting techniques, including chain-of-thought reasoning, role-playing, and structured questioning methods. Directly relevant to understanding the theoretical foundation behind the four strategies presented in the session.
Chain-of-Thought Prompting Research Paper
Academic paper introducing chain-of-thought reasoning in large language models. Provides scientific backing for the stepwise methodology Holland demonstrates as Strategy 3, explaining why controlled progression improves AI reasoning quality.
Few-Shot Learning and In-Context Learning
Research on how language models adapt behavior based on examples and context. Relevant to understanding why role-playing (Strategy 4) and contextual Q&A prompting (Strategy 1) produce enhanced AI performance.
Developer Productivity and AI-Assisted Development
GitHub Copilot Research: Productivity Impact Study
Empirical research on developer productivity improvements with AI assistance. Provides quantitative context for the qualitative improvements demonstrated through strategic prompting techniques.
Software Development with Large Language Models
Academic analysis of LLM integration in software development workflows. Offers theoretical framework for understanding how strategic prompting transforms AI from autocomplete tool to collaborative development partner.
AI-Assisted Programming: A Survey
Comprehensive survey of AI applications in programming, including code generation, debugging, and architecture design. Provides broader context for the specific techniques demonstrated in the session.
Voice Recognition and Natural Language Processing
Whisper: Robust Speech Recognition
OpenAI’s research on advanced speech recognition systems. Relevant to understanding the local speech processing capabilities that enable the voice-first interaction approach Holland advocates.
Speech-to-Code: Converting Natural Language to Programming Languages
Research on natural language to code conversion, providing context for why voice-first interaction with AI coding assistants produces superior results compared to traditional text-based interfaces.
Project Architecture and Code Organization
Clean Architecture Principles
Robert C. Martin’s seminal work on software architecture organization. Provides foundation for understanding why the Q&A prompting method for project structure organization aligns with established architectural best practices.
Design Patterns: Elements of Reusable Object-Oriented Software
Classic reference for software design patterns, including Singleton and other patterns discussed in the pros and cons analysis demonstration. Essential for understanding the architectural decisions facilitated by strategic AI prompting.
Martin Fowler’s Refactoring Catalog
Comprehensive resource on refactoring techniques and methodologies. Directly relevant to understanding why the stepwise chain-of-thought approach improves refactoring safety and effectiveness.
Educational Technology and AI Tutoring
Intelligent Tutoring Systems Research
Academic journal covering AI-powered educational systems. Provides research foundation for understanding why role-playing AI tutors (Strategy 4) produce effective learning outcomes.
Personalized Learning with AI
Overview of AI applications in personalized education. Relevant to understanding how role-playing prompts can be adapted for individualized learning experiences across various subjects.
Professional Development and Career Enhancement
The DevOps Handbook
Comprehensive guide to DevOps practices and cultural transformation. Provides context for how AI-assisted development practices demonstrated in the session align with modern software development methodologies.
Pragmatic Programmer
Classic software development philosophy that emphasizes tool mastery and productivity optimization. The strategic prompting techniques align with the pragmatic philosophy of using appropriate tools to maximize development effectiveness.
Each reference provides specific value for different aspects of implementing and understanding the strategic AI prompting techniques demonstrated in Burke Holland’s session, from technical implementation details to theoretical foundations and broader applications across professional development contexts.
Appendix
A. Technical Specifications and Implementation Details
GitHub Copilot Mode Comparison:
Ask Mode Characteristics:
- Input Processing: Natural language queries with optional context modifiers
- Output Format: Structured responses, code examples, and explanatory text
- Context Awareness: File-level and project-level understanding via @codebase
- Response Time: Near-instantaneous for analysis and recommendations
- Use Cases: Strategy development, analysis, documentation generation
Agent Mode Capabilities:
- Autonomous Operation: Multi-file modifications without constant user intervention
- Context Persistence: Maintains understanding across multiple related operations
- File System Access: Can read, analyze, and modify multiple project files
- Safety Mechanisms: Built-in protections against destructive operations
- Integration Depth: Deep IDE integration with project structure awareness
Edit Mode Features:
- Targeted Modifications: Cursor-position-aware code suggestions
- Real-time Integration: Live collaboration during active coding sessions
- Context Sensitivity: Understanding of immediate code environment
- Incremental Changes: Small, focused modifications to existing code
- Undo Integration: Seamless integration with editor undo/redo functionality
B. Voice Recognition Technical Architecture
Local Speech Processing Pipeline:
- Audio Capture: Microphone input processing with noise reduction
- Feature Extraction: Audio signal processing for speech recognition
- Model Inference: Local neural network processing for transcription
- Text Output: Formatted text delivery to chat interface
- Context Integration: Seamless integration with existing conversation threads
Performance Characteristics:
- Latency: Sub-second processing for real-time interaction
- Accuracy: High precision across technical vocabulary and casual speech
- Resource Usage: Minimal CPU/memory overhead for local processing
- Language Support: Multiple language recognition capabilities
- Offline Capability: No internet dependency for speech recognition
C. Detailed Prompt Templates and Variations
Q&A Prompt Variations:
Basic Project Structure:
"I have [project description] that needs better organization.
Before recommending a structure, please ask me [X] questions
that will help you understand my specific requirements and constraints."Technology Decision Making:
"I need to choose [technology/framework/tool] for [specific use case].
Instead of just giving me a recommendation, please ask me questions
about my requirements, constraints, and preferences first."Architecture Planning:
"I'm designing [system/application] and need architectural guidance.
Please interview me with targeted questions to understand my
scalability, performance, and maintainability requirements."Pros and Cons Prompt Templates:
Implementation Decision:
"I need to implement [specific functionality] in my [context].
What are several different approaches I could take?
Please give me the pros and cons of each option so I can make an informed decision."Technology Selection:
"I'm trying to decide between [list of options] for [specific purpose].
Can you analyze each option and provide detailed pros and cons
considering factors like [performance/cost/maintenance/learning curve]?"Stepwise Chain of Thought Templates:
Refactoring Control:
"I want to refactor [specific code/system]. Please move one step at a time,
explaining each step before implementing it. Do not proceed to the next step
until I give you the keyword '[unique_keyword]'."Feature Implementation:
"I need to implement [complex feature] but want to do it incrementally.
Break this down into discrete steps and wait for my approval
(keyword: '[unique_keyword]') before moving to each next step."Role Playing Prompt Variations:
Expert Instructor:
"You are a world-class [subject] instructor known for [specific teaching qualities].
You teach through [methodology] and your students love your class because [reasons].
Today you're teaching [specific topic]. Start the lesson and provide interactive exercises."Technical Consultant:
"You are a senior [role] consultant with [X] years of experience in [domain].
You're known for [specific expertise] and your ability to [key strengths].
I need your advice on [specific situation]. Please ask clarifying questions
and provide expert guidance."Code Review Expert:
"You are a principal software engineer who excels at code reviews.
You're known for finding both technical issues and opportunities for improvement.
Please review [code/architecture] and provide feedback in your characteristic
thorough but constructive style."D. Advanced Integration Patterns
Cross-Mode Workflow Examples:
Complete Feature Development:
- Ask Mode: Q&A prompting for requirements gathering
- Ask Mode: Pros and cons analysis for implementation approach selection
- Agent Mode: Autonomous implementation of selected approach
- Edit Mode: Fine-tuning and optimization of generated code
Architecture Refactoring:
- Ask Mode: Role-playing architectural consultant for strategy development
- Ask Mode: Stepwise planning with controlled progression keywords
- Agent Mode: Implementation of each step with validation checkpoints
- Edit Mode: Manual adjustments and quality improvements
Learning and Skill Development:
- Ask Mode: Role-playing expert instructor for lesson planning
- Ask Mode: Interactive exercises with guided discovery methodology
- Edit Mode: Hands-on practice with real-time feedback
- Agent Mode: Project implementation applying learned concepts
E. Troubleshooting and Optimization Guidelines
Common Issues and Solutions:
Context Confusion:
- Problem: AI loses track of conversation thread or mixes different problems
- Solution: Regular chat clearing and explicit context restatement
- Prevention: Use @codebase and other context modifiers consistently
Overwhelming Output:
- Problem: AI generates excessive code or information in single response
- Solution: Implement stepwise progression with control keywords
- Prevention: Explicitly request incremental approaches in initial prompts
Generic Responses:
- Problem: AI provides general advice instead of specific, contextual recommendations
- Solution: Enhance prompts with project-specific context and constraints
- Prevention: Always include @codebase modifier and specific technical details
Role Playing Inconsistency:
- Problem: AI breaks character or provides inconsistent expert persona responses
- Solution: Reinforce role definition and behavioral expectations mid-conversation
- Prevention: Provide detailed persona descriptions including communication style and expertise boundaries
F. Extended Application Scenarios
Enterprise Development Applications:
Legacy System Modernization:
- Q&A Analysis: Understanding current system constraints and modernization goals
- Pros and Cons: Evaluation of migration strategies and technology choices
- Stepwise Implementation: Controlled progression through modernization phases
- Expert Consultation: Role-playing system architects and domain experts
Team Onboarding and Training:
- Interactive Documentation: Role-playing technical writers for comprehensive guides
- Skill Assessment: Q&A prompting for competency evaluation and gap identification
- Progressive Learning: Stepwise skill development with controlled complexity increase
- Mentorship Simulation: Expert persona guidance for junior developer support
Quality Assurance and Testing:
- Test Strategy Development: Pros and cons analysis of testing approaches
- Test Case Generation: Q&A prompting for comprehensive scenario coverage
- Bug Triage: Role-playing QA experts for issue prioritization and analysis
- Process Improvement: Stepwise implementation of quality enhancement initiatives
This appendix provides comprehensive technical details, implementation templates, and advanced usage patterns that complement the core strategies demonstrated in Burke Holland’s session, enabling practitioners to adapt and extend these techniques across diverse development and professional contexts.