Kritrima Prajna skill that every student must know

🔹 Core Skills (Still Mandatory – Baseline Expectations)

These are no longer differentiators, but minimum requirements:

1. Data Structures & Algorithms (DSA)

   • Time–space tradeoff analysis

   • Problem-solving approach (not just final code)

2. System Design Fundamentals

   • Scalable architecture

   • Component interaction and bottleneck analysis

3. Debugging Skills

   • Identifying logical and runtime errors

   • Validating AI-generated code (AI often makes logic mistakes)

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🔹 AI-Era Differentiator Skills (New Expectations)

These skills separate strong candidates from average ones:

4. Prompt Engineering

   • Writing effective prompts to guide AI tools

   • Refining prompts for better code, tests, and explanations

5. AI-Assisted Coding

   • Using AI for boilerplate code

   • Accelerating development without blindly trusting output

6. AI-Assisted Debugging

   • Using AI to trace errors in large codebases

   • Asking the right questions to AI for root-cause analysis

7. Error Handling & Validation

   • Detecting hallucinations or incorrect assumptions by AI

   • Verifying correctness, edge cases, and constraints

8. Engineering Judgment with AI

   • Knowing when to use AI vs when traditional coding is better

   • Combining human reasoning with AI suggestions effectively

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🔹 Advanced System & AI Integration Skills

Increasingly tested in system design interviews:

9. Integrating AI into Existing Systems

   • Adding AI features into real-world workflows

   • API-based model integration

10. Model Lifecycle Management

• Versioning models

• Monitoring performance and drift

• Updating or rolling back models

11. Trade-off Analysis for AI Systems

• Cost vs performance

• Reliability vs speed

• Scalability vs complexity

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🔹 Interview-Specific Practical Skills

Now commonly expected in interviews:

12. Using AI During Live Coding

• Efficient collaboration with AI tools

• Demonstrating reasoning, not copy-paste coding

13. Rapid Feature Delivery

• Building or extending a feature in a short time (≈1 hour)

• Managing unfamiliar codebases with AI support

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🔹 Key Mindset Shift (Most Important)

14. AI as a Co-Engineer, Not a Crutch

• AI handles repetitive tasks

• Engineers focus on:

  • Business logic

  • System architecture

  • Decision-making

  • Quality assurance

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🔑 One-Line Summary

In the AI era, companies expect engineers who can think deeply, design systems intelligently, and use AI tools strategically—not just code well.