Test Your knowledge - Quiz 1

Vibe Coding Framework: Test Your Knowledge

Questions

1. What does the term "vibe programming" describe in the context of software development?

2. What are the five core principles of the Vibe Coding Framework's philosophy?

3. What does the S.C.A.F.F. acronym stand for in the Prompt Engineering System?

4. According to the framework, what is one of the biggest risks of AI-assisted development that the Documentation Generator aims to address?

5. Name three common security challenges that AI-generated code presents according to the Security Toolkit.

6. What does the V.E.R.I.F.Y. protocol framework stand for?

7. What is the primary purpose of the "Verbalize" step in the V.E.R.I.F.Y. protocol?

8. What are the six steps in the R.E.F.A.C.T. methodology for refactoring AI-generated code?

9. According to the framework, what is the Dunning-Kruger effect in the context of vibe programming?

10. What are the three verification levels defined in the framework, and how do they differ?

11. What documentation methodology does the framework recommend, and what does its acronym stand for?

12. What collaboration model does the framework recommend for distributed teams working across different time zones?

13. What is the recommended implementation timeline for individual developers adopting the framework?

14. Name three metrics the framework suggests for measuring the effectiveness of your prompt engineering.

15. What is the S.H.I.E.L.D. security methodology mentioned in the framework?

16. What are the five components of the C.O.D.E.S. collaboration model?

17. According to the framework, what is one way to address the challenge of "verification fatigue"?

18. What is the recommended enterprise-wide implementation timeline for the framework?

19. What specific cognitive bias does the framework highlight as particularly dangerous in AI-assisted development?

20. What versioning approach does the Vibe Programming Framework follow?

Answers

1. "Vibe programming" describes the practice of using AI tools to generate code based on high-level, natural language prompts rather than writing every line manually. The term captures the intuitive, conversational nature of this approach—developers communicate the general direction or "vibe" of what they want to build, and AI systems transform these intentions into functional code.

2. The five core principles are:

   • Augmentation, Not Replacement

   • Verification Before Trust

   • Maintainability First

   • Security by Design

   • Knowledge Preservation

3. S.C.A.F.F. stands for:

   • Situation (establish development context)

   • Challenge (define the specific coding task)

   • Audience (specify who will work with the code)

   • Format (define expected structure and style)

   • Foundations (specify security and quality requirements)

4. Knowledge gaps and technical debt. The Documentation Generator addresses the risk of losing understanding of how AI-generated code works, which can lead to maintenance challenges over time.

5. Three common security challenges of AI-generated code:

   • Pattern Replication (AI models may reproduce security anti-patterns from training data)

   • Default Insecurity (Generated code often prioritizes functionality over security)

   • Obscured Vulnerabilities (Security issues may be hidden within seemingly functional code)

   • False Confidence (Well-formatted code creates a false sense of security)

   • Incomplete Context (AI lacks complete understanding of security requirements)

6. V.E.R.I.F.Y. stands for:

   • Verbalize

   • Examine Dependencies

   • Review Security Implications

   • Inspect Edge Cases

   • Functional Validation

   • Yield Improvements

7. The "Verbalize" step requires developers to explain the code's operation in their own words, articulating the overall purpose, how each function works, data flow, and edge case handling. This step tests true comprehension rather than the ability to repeat explanations.

8. The six steps in R.E.F.A.C.T. are:

   • Recognize Patterns

   • Extract Components

   • Format for Readability

   • Address Edge Cases

   • Confirm Functionality

   • Tune Performance

9. The Dunning-Kruger effect in vibe programming refers to developers with limited knowledge overestimating their competence in evaluating AI-generated code. This creates a dangerous scenario where developers confidently deploy code with hidden vulnerabilities, inefficiencies, or logical errors because they lack the expertise to identify these issues.

10. The three verification levels are:

    • Level 1 (Basic Verification): For low-risk components, performed by individual developers with focus on basic functionality

    • Level 2 (Standard Verification): For typical production code, includes all V.E.R.I.F.Y steps, unit tests, security scanning, and peer review

    • Level 3 (Enhanced Verification): For high-risk components (authentication, payments), includes in-depth security review, comprehensive testing, formal security review, and multiple sign-offs

11. The D.O.C.S. methodology, which stands for:

    • Design Decisions (document key architectural choices)

    • Operational Context (capture knowledge needed to work with the code)

    • Code Understanding (provide explanations for complex sections)

    • Support Information (include troubleshooting and maintenance guidance)

12. For distributed teams, the framework recommends:

    • Asynchronous Collaboration Practices (comprehensive documentation, verification request system, knowledge base structure)

    • Synchronous Touchpoints (overlap window sessions, video reviews, virtual pair programming)

    • Tools and Infrastructure (collaborative documentation, verification tracking, prompt management)

13. The recommended implementation timeline for individual developers is 90 days, divided into:

    • Phase 1: Foundation (Days 1-30) - establish core practices

    • Phase 2: Proficiency (Days 31-60) - deepen implementation and expand to advanced practices

    • Phase 3: Mastery (Days 61-90) - optimize personal implementation and measure impact

14. Three metrics for measuring prompt effectiveness:

    • First-Attempt Success Rate: Percentage of prompts that produce usable code on first try

    • Iteration Efficiency: Average number of refinements needed to reach production-ready code

    • Comprehension Index: How easily developers can understand and explain generated code

    • Security Score: How well the generated code adheres to security best practices

    • Maintenance Rating: How maintainable the code remains after 3/6/12 months

15. S.H.I.E.L.D. stands for:

    • Secure by Design Prompting

    • Hardening Review Process

    • Injection Prevention Patterns

    • Encryption and Data Protection

    • Least Privilege Enforcement

    • Defence-in-Depth Strategy

16. The five components of C.O.D.E.S. are:

    • Collective Prompt Engineering

    • Open Verification Process

    • Distributed Knowledge Preservation

    • Established Governance

    • Skill Development Balance

17. To address "verification fatigue," the framework recommends:

    • Breaking reviews into manageable sessions

    • Alternating between different types of review activities

    • Using the layered approach to maintain focus

    • Leveraging automated tools to reduce manual burden

18. The recommended enterprise-wide implementation timeline is 12 months, divided into four phases:

    • Phase 1: Foundation (Months 1-3)

    • Phase 2: Expansion (Months 4-6)

    • Phase 3: Standardization (Months 7-9)

    • Phase 4: Optimization (Months 10-12)

19. The Dunning-Kruger effect is specifically highlighted as particularly dangerous in AI-assisted development, where developers with limited knowledge overestimate their ability to evaluate AI-generated code.

20. The framework follows semantic versioning (SemVer) with format MAJOR.MINOR.PATCH:

    • MAJOR version (X.0.0): For backward-incompatible changes requiring significant adaptation

    • MINOR version (0.X.0): For backward-compatible functionality additions

    • PATCH version (0.0.X): For backward-compatible bug fixes and minor refinements