Prompt Engineering

Prompt engineering is the art and science of communicating effectively with AI. With Claude Code, this isn’t just about getting answers – it’s about orchestrating complex development workflows. This guide reveals the advanced prompting strategies that transform Claude from a coding assistant into a sophisticated development partner.

The Psychology of Prompting Claude

Understanding Claude’s Mental Model

Claude Code operates as what Anthropic engineers call a “fast intern with perfect memory.” This mental model is crucial for effective prompting:

Claude's Characteristics

• Eager to help: Responds well to clear, specific instructions
• Perfect recall: Remembers everything in the conversation context
• Limited experience: May need guidance on architectural decisions
• Literal interpretation: Benefits from explicit constraints and requirements
• Context-aware: Uses codebase knowledge to inform responses

The Progressive Disclosure Pattern

One of the most powerful techniques is revealing information gradually:

# ❌ Information overload
> Build a complete e-commerce platform with user auth, product catalog,
  shopping cart, payment processing, order management, inventory tracking,
  email notifications, and admin dashboard using Next.js and PostgreSQL

# ✅ Progressive disclosure
> First, analyze our current codebase structure
# Claude examines existing patterns

> Create a plan for adding e-commerce features to our platform
# Claude creates phased approach

> Let's start with the product catalog. Design the database schema
# Claude focuses on one component at a time

Advanced Prompting Patterns

The Think-Plan-Execute Pattern

Leverage Claude’s extended reasoning capabilities strategically:

Basic Thinking

> think about how to implement rate limiting
# Allocates ~1,000 tokens for reasoning
# Good for straightforward problems

Deep Thinking

> think hard about the microservices architecture
# Allocates ~5,000 tokens for reasoning
# Ideal for complex architectural decisions

Ultra Thinking

> ultrathink about optimizing our database for scale
# Allocates ~128,000 tokens for reasoning
# Reserve for critical, complex problems

The Constraint-First Pattern

Provide constraints before requirements to frame the solution space:

# Effective constraint-first prompting
> Constraints:
  - Must maintain backward compatibility
  - Cannot modify existing database schema
  - Must complete in under 2 seconds
  - Limited to 50MB memory usage

  Task: Optimize the search functionality

# Claude now understands the boundaries before solving

The Example-Driven Pattern

Show Claude what you want through examples:

> Here's how we currently handle authentication:
  [paste existing auth code]

  Here's a similar system that uses JWT:
  [paste JWT example]

  Migrate our auth to use JWT following the same patterns

# Claude learns from concrete examples

Prompting for Different Development Phases

Discovery and Analysis Prompts

Architecture Exploration

"Analyze the dependency graph between
our services and identify potential
circular dependencies or tight coupling"

Performance Analysis

"Profile the codebase and identify
the top 5 performance bottlenecks
with specific line numbers"

Security Audit

"Review authentication flow for
OWASP top 10 vulnerabilities and
suggest remediations"

Technical Debt

"Find code smells and anti-patterns
in our codebase, prioritized by
impact and effort to fix"

Implementation Prompts

The key to implementation prompts is specificity without micromanagement:

# Too vague
> Add caching

# Too prescriptive
> Add Redis caching using node-redis client version 4.5.1 with...

# Just right
> Add caching to our API responses:
  - Use our existing Redis setup
  - Cache GET endpoints for 5 minutes
  - Invalidate on related POST/PUT/DELETE
  - Follow our established error handling patterns

Testing and Quality Prompts

# Comprehensive test generation
> Generate tests for the payment module:
  - Unit tests for each function
  - Integration tests for API endpoints
  - Edge cases including network failures
  - Mock external payment gateway
  - Aim for 90%+ coverage

# Intelligent test improvement
> Review our existing tests and:
  - Identify missing edge cases
  - Find flaky tests and make them deterministic
  - Add property-based tests where appropriate
  - Ensure tests follow AAA pattern

Complex Task Orchestration

The Checklist Pattern

For multi-step operations, use structured checklists:

> Create migration-checklist.md with all steps needed to:
  1. Migrate from Express to Fastify
  2. Maintain all existing functionality
  3. Update tests
  4. Update documentation

  Then work through the checklist, checking off each item

# Claude creates and follows structured plan

The Verification Loop Pattern

Build verification into your prompts:

> For each file you modify:
  1. Make the change
  2. Run related tests
  3. If tests fail, fix and re-run
  4. Only move to next file when tests pass

  Start with user.service.ts

The Parallel Subtask Pattern

Leverage Claude’s subagent capabilities:

> Use subagents to investigate these in parallel:
  - Current memory usage patterns in the app
  - Available caching strategies for our stack
  - Performance benchmarks from similar systems

  Then synthesize findings into optimization plan

Language and Context Optimization

Token-Efficient Prompting

Verbose (High Token)

"Could you please help me understand how I might
go about potentially refactoring this authentication
module to possibly use JWT tokens instead of the
current session-based approach, making sure to
maintain all existing functionality?"

~50 tokens

Concise (Low Token)

"Refactor auth: sessions → JWT.
Keep all functionality."

~12 tokens

Structured (Optimal)

"Auth refactor:
FROM: Session-based
TO: JWT
PRESERVE: All endpoints, tests"

~15 tokens, maximum clarity

Context Window Management

Manage Claude’s context strategically:

# Clear context between major tasks
> /clear

# Selective context loading
> Focus only on the payment module - ignore other parts
> Analyze payment/src/ for PCI compliance issues

# Context preservation
> Before we continue, summarize what we've accomplished
  and what remains in todo.md
> /clear
> Continue from todo.md

Specialized Prompting Techniques

The Socratic Method

Guide Claude to better solutions through questions:

> What are the potential issues with this database schema?
# Claude identifies problems

> How would you solve the N+1 query problem you identified?
# Claude proposes solutions

> What are the tradeoffs of each approach?
# Claude analyzes options

> Implement the solution that best balances performance and maintainability
# Claude makes informed choice

The Rubber Duck Pattern

Use Claude as an intelligent rubber duck:

> I'm trying to debug why our WebSocket connections drop after exactly
  30 seconds. Here's what I know:
  - Only happens in production
  - Started after last deployment
  - No error logs

  Help me think through possible causes

# Claude asks clarifying questions and guides investigation

The Code Review Pattern

Structure code review requests effectively:

> Review this PR focusing on:
  1. Logic errors and edge cases
  2. Security vulnerabilities
  3. Performance implications
  4. Consistency with our patterns

  Skip style issues - linter handles those

  [paste diff or reference PR]

Prompt Templates and Reusability

Creating Effective Slash Commands

Store complex prompts as reusable commands:

.claude/commands/optimize-query.md

Analyze the SQL query provided and:

1. Explain the current execution plan
2. Identify performance bottlenecks
3. Suggest optimizations with explanations
4. Provide the optimized query
5. Estimate performance improvement

Consider:
- Index usage
- Join strategies
- Data volume
- Query frequency

Arguments: $ARGUMENTS

Usage:

> /project:optimize-query "SELECT * FROM users WHERE..."

Dynamic Prompt Generation

Use shell scripting to create context-aware prompts:

# Generate prompt with current system state
claude << EOF
System status:
- CPU: $(top -l 1 | grep "CPU usage" | awk '{print $3}')
- Memory: $(vm_stat | grep "Pages free" | awk '{print $3}')
- Disk: $(df -h / | awk 'NR==2 {print $5}')

Diagnose why the application is running slowly
EOF

Error Recovery and Iteration

The Graceful Failure Pattern

Build error handling into prompts:

> Try to implement OAuth integration:
  - If any step fails, document why in errors.log
  - Suggest alternative approaches
  - Create a minimal working version if full implementation isn't possible
  - Mark unclear requirements with TODO comments

The Iterative Refinement Pattern

Don’t expect perfection on first try:

# Initial attempt
> Create a user registration form

# Refinement based on output
> Good start. Now add:
  - Client-side validation
  - Password strength indicator
  - Terms of service checkbox

# Further refinement
> Add proper error handling for when the API is unavailable

Advanced Prompt Engineering Strategies

The Persona Pattern

Invoke specific expertise:

> As a database performance expert, analyze our query patterns

> As a security researcher, attempt to find vulnerabilities

> As a UX designer, suggest improvements to our error messages

The Debate Pattern

Use conflicting viewpoints for better solutions:

> Present arguments for and against using microservices
  for our use case, then recommend the best approach

> Compare REST vs GraphQL for our mobile app,
  considering our specific constraints

The Meta-Prompting Pattern

Have Claude help improve your prompts:

> Here's a task I often need to do:
  [describe task]

  Create an optimized prompt template I can reuse

# Claude creates refined, reusable prompt

Tip

Remember: Claude Code has access to your entire conversation history within a session. Reference earlier work naturally: “Using the schema we designed earlier…” or “Following the patterns we established…”

Measuring Prompt Effectiveness

Key Metrics

Track these to improve your prompting:

1. Token efficiency: Output quality / tokens consumed
2. Iteration count: How many refinements needed
3. Error rate: How often Claude misunderstands
4. Time to solution: Total time from prompt to working code

Prompt Optimization Checklist

Before sending a complex prompt:

• Is the task clearly defined?
• Are constraints explicit?
• Are success criteria measurable?
• Is context focused and relevant?
• Are examples provided where helpful?
• Is the prompt structured logically?
• Are edge cases considered?

Common Antipatterns to Avoid

❌ The Novel

Writing extremely long, rambling prompts that bury the actual request in paragraphs of context.

❌ The Mind Reader

Assuming Claude knows your unstated preferences, architectural decisions, or business logic.

❌ The Perfectionist

Expecting flawless code on first generation instead of iterating.

❌ The Micromanager

Specifying every implementation detail instead of leveraging Claude’s capabilities.

What’s Next?

Master these complementary skills:

• Batch Operations - Apply prompts at scale
• Debugging Strategies - Prompting for effective debugging
• Review Automation - Automated code quality prompts

Remember: Prompt engineering is both art and science. The best prompts are clear, contextual, and conversational. With practice, you’ll develop an intuition for what works and create your own patterns that match your development style.