Beyond Basic Prompting
Prompt engineering has evolved considerably with the arrival of skills. It is no longer simply about formulating good questions, but about building an instruction system that amplifies every interaction with AI.
Fundamentals of Modern Prompt Engineering
The Specificity Principle
The more specific your instructions, the better the results. Compare:
Level 1 - Vague:
"Help me write React code"
Level 2 - Specific:
"Create a functional React component in TypeScript that displays a paginated user list with sorting by name and search"
Level 3 - With context (skill):
The skill already provides context (React 18, strict TypeScript, Tailwind, pagination with React Query), so you can simply say: "Create the user list component"
The Power of Persistent Context
Skills transform prompt engineering by adding a permanent context layer. Every prompt automatically benefits from:
- Knowledge of the tech stack
- Code conventions
- Preferred patterns
- Constraints to follow
Advanced Techniques
1. Chain of Thought in Skills
Guide Claude's reasoning directly through the skill:
## Development Process
When asked for a feature:
1. Analyze existing components that could be reused
2. Propose the architecture before coding
3. Implement with tests
4. Document technical choices
2. Few-Shot Examples
Include concrete examples in your skill to show expected output:
## Expected Code Style
### Service Example
```typescript
export class UserService {
constructor(private readonly repo: UserRepository) {}
async findById(id: string): Promise<User> {
const user = await this.repo.findOne(id);
if (!user) throw new NotFoundException('User not found');
return user;
}
}
Test Example
describe('UserService', () => {
it('should throw when user not found', async () => {
repo.findOne.mockResolvedValue(null);
await expect(service.findById('xxx')).rejects.toThrow();
});
});
### 3. Negative Constraints
Instructions about what **not** to do are often more effective than positive instructions:
```markdown
## Anti-patterns to Avoid
- NEVER console.log in production
- NEVER TODO without associated ticket
- NEVER magic numbers without named constant
- NEVER functions longer than 30 lines without decomposition
- NEVER empty catch blocks (always log or rethrow)
4. Role Prompting via Skills
Define Claude's role in your skill:
## Role
You are a senior developer specialized in microservices architecture.
You prioritize:
- Separation of concerns
- Clear interfaces between services
- Resilience and error handling
- Code testability
5. Response Templates
Structure expected responses:
## Response Format for Code Reviews
When I ask for a review, respond with:
1. **Summary**: Overview in 2-3 sentences
2. **Positives**: What is well done
3. **Improvements**: What can be improved (sorted by priority)
4. **Security**: Potential security alerts
5. **Performance**: Optimization suggestions
Combining Prompt Engineering and Skills
The Context Pyramid
Think of your instruction system as a pyramid:
- Base: Global skills (company conventions)
- Middle: Project skills (stack, architecture)
- Top: One-off prompts (specific requests)
The lower you go in the pyramid, the more stable and reused the instructions. The higher you go, the more specific and ephemeral they are.
The Art of Composition
An expert prompt combines skill context with a precise request:
Bad: "Make an API" Good: "Implement the POST /api/orders endpoint with validation, error handling, and integration test"
The skill already provides the HOW, your prompt should provide the WHAT.
Measuring Prompt Effectiveness
Metrics to Track
- First-shot rate: Does the generated code work on the first try?
- Number of corrections: How many iterations are needed?
- Relevance: Are suggestions aligned with your stack?
- Completeness: Are tests and documentation included?
Iterative Optimization
Keep a journal of your interactions:
- Note cases where Claude does not understand your intent
- Identify recurring patterns
- Add corresponding rules to your skill
- Measure the improvement
Common Prompt Engineering Mistakes
1. Overloading Context
Too many instructions kills the instruction. Keep your skills concise and relevant.
2. Contradictions in Skills
Check that your different skills do not contradict each other. For example, one skill saying "always use classes" and another "always use functions."
3. Ignoring Feedback
If Claude regularly makes the same mistake, it is a signal to improve your skill, not to repeat the same prompt.
Conclusion
Advanced prompt engineering with skills is a productivity multiplier. By investing in the quality of your persistent instructions, every interaction with AI becomes more effective.
Explore our skills library to find optimized templates and check our practical guides to keep progressing.