AI Engineering Cohort: https://aiengg.dev/ Github resources: https://github.com/InterviewReady/ai-engineering-resources On 6th June 2026, we are launching our first AI Engineering Cohort. A focused, intense 8-week program for software engineers who want real, production-grade AI skills. More details coming soon. Cheers!

The title is descriptive enough! What's going on in the world of AI? Github resources: https://github.com/InterviewReady/ai-engineering-resources ---------------------------------------- 00:00 Checks 00:40 Who is Tanishq Singh? 02:45 1. What do you look for in an AI Engineer resume? 07:35 2. How do you test the claims of a candidate? 11:04 3. Should Software devs be worried about AI? 15:55 4. Is DSA required for an AI engineer interview? 18:46 5. Is getting a job harder now for freshers? 22:24 6. Is the token budget for companies making sense? 29:29 7. How do we stay updated in the world of AI? 33:03 8. CEO asks me to use AI and not write code? 34:37 9. AI cohort useful for a 10-year experienced engineer? 35:26 10. What AI topics should we know in 2026? 38:20 11. Can I move from SWE to AI or FDE? 40:00 12. AI Resources to use 40:30 13. What depth is needed to become an AI engineer? 42:55 14. AI Cohort Overview AI Engineering Cohort: https://aiengg.dev/ On 6th June 2026, we are launching our first AI Engineering Cohort. A focused, intense 8-week program for software engineers who want real, production-grade AI skills. More details coming soon. Cheers!

Cohort Link: https://aiengg.dev 00:00 Introduction 00:48 1. Hallucinations in AI 04:28 2. Evals and Tool Calling 13:45 3. Taking AI to 100% Automation 20:33 4. What will happen in 5 years? 27:30 5. Is DSA Dead? 28:23 6. Is AGI Coming 29:55 7. Steps to Learn AI as an SDE 33:16 Thank you! Core Theme: Problem Definition & Evaluation The key to successful AI systems isn't the model—it's how well you define the problem and evaluate the solution. Problem definition accounts for 30% of success, evaluation 70%. Main Points: Problem Definition - Be explicit about constraints, tech stack, success metrics, and requirements. Don't give vague instructions to AI; frame the problem like you would for a human engineer. Context Matters - Hallucinations don't just come from models; bad context you provide causes them. Scope context to what's actually needed for the problem. Evaluation is Everything - Most engineers evaluate AI systems on output alone. The 5% winners evaluate on tool-calling sequences, decision-making logic, and whether the system followed proper patterns—not just whether the final answer looks right. Example: Booking a Flight - A bad system just books the ticket (95% metric). A good system first gathers user data, identifies missing information (date, preferences, airline), asks clarifying questions, then books (5% thinking). Subject Matter Experts Are Critical - Don't automate without verification. Sit with domain experts (nurses for healthcare, aerospace engineers for aviation) to validate prompts and instructions before deployment. Don't Overcomplicate - Focus on fundamentals, not frameworks. Don't use LLM-as-judge as a cop-out; that's lazy when you should have ground truth and proper evaluation. Bottom Line: Success comes from rigorous problem scoping, proper evaluation metrics, and subject matter expert involvement.

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