💻Criticalmedium15-20 minutes
Have you built any AI-powered applications (LLM, RAG, agents, or similar)?
technicalllmragagentsmodern-aiagentic-workflowscritical
🎯 What Interviewers Are Looking For
- ✓Practical experience with modern AI technologies (LLMs, RAG)
- ✓Understanding of how to build complete applications, not just run models
- ✓Real-world problem-solving with AI
- ✓Awareness of challenges like latency, cost, prompt engineering
📋 STAR Framework Guide
Structure your answer using this framework:
S - Situation
What AI application did you build? What problem did it solve?
T - Task
What were the technical requirements and constraints?
A - Action
How did you architect and implement it? What technologies did you use?
R - Result
What was the outcome? What challenges did you overcome?
💬 Example Answer
⚠️ Pitfalls to Avoid
- ✗Saying "no" without elaborating on related experience
- ✗Only mentioning that you used ChatGPT API without showing understanding
- ✗Not explaining the architecture or technical decisions
- ✗Claiming experience you don't have (they will dig deeper)
- ✗Focusing only on the model without discussing the application layer
- ✗Not acknowledging limitations or challenges you faced
💡 Pro Tips
- ✓If you have LLM/RAG experience: explain architecture, challenges, and tradeoffs
- ✓If you don't: connect related experience (like sentiment analysis → LLM APIs)
- ✓Show you understand key concepts: prompt engineering, context management, RAG, vector databases
- ✓For agentic workflows: explain tool use, ReAct pattern, multi-step planning, and guardrails
- ✓Mention practical concerns: latency, cost, hallucinations, safety, agent loop limits
- ✓Be honest about what you've built vs what you've experimented with
- ✓Demonstrate learning: "I haven't built X yet, but I've been studying Y and Z"
- ✓Connect to the role: "I'm excited to work on [company's LLM product]"
- ✓Discuss frameworks: LangChain, LangGraph, AutoGen, CrewAI and when to use each
🔄 Common Follow-up Questions
- →What was the most challenging part of building that application?
- →How did you handle prompt engineering and prevent hallucinations?
- →What vector database did you use for RAG? Why?
- →How did you optimize costs when using LLM APIs?
- →What metrics did you use to evaluate your AI application?
- →Have you worked with fine-tuning LLMs?
- →How do you implement guardrails to prevent agent runaway loops?
- →What's your approach to multi-agent orchestration?
- →How do you handle tool selection and function calling in agents?
🎤 Practice Your Answer
0:00
Target: 2-3 minutes
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