💻Criticalmedium20-25 minutes
What LLM models have you used? Why did you choose them?
llmmodel-selectionopenaiclaudellamacriticalmodern-ai
🎯 What Interviewers Are Looking For
- ✓Hands-on experience with modern LLMs (not just theory)
- ✓Understanding of model tradeoffs (cost, latency, quality)
- ✓Practical decision-making (not just 'GPT-4 is best')
- ✓Awareness of the LLM landscape (OpenAI, Anthropic, open source)
- ✓Production considerations (API vs self-hosted, pricing, rate limits)
📋 STAR Framework Guide
Structure your answer using this framework:
S - Situation
What LLM-powered applications or experiments have you built?
T - Task
What were the requirements and constraints?
A - Action
Which LLMs did you evaluate and why did you choose specific ones?
R - Result
How did they perform? What did you learn about LLM selection?
💬 Example Answer
⚠️ Pitfalls to Avoid
- ✗Saying you've only used ChatGPT (shows limited hands-on experience)
- ✗Claiming you've used models you actually haven't (interviewers will dig deeper)
- ✗Not explaining why you chose specific models (just listing names)
- ✗Only knowing OpenAI models (shows limited awareness of the landscape)
- ✗Not mentioning cost, latency, or practical constraints
- ✗Saying 'GPT-4 is always the best' without nuance
- ✗Not having any specific examples or projects to reference
💡 Pro Tips
- ✓Be honest about which models you've actually used hands-on
- ✓Explain the use case for each model choice
- ✓Show awareness of tradeoffs: quality vs cost vs latency vs control
- ✓Mention at least one open-source model (shows broad awareness)
- ✓Include specific numbers: cost per 1M tokens, context length, latency
- ✓Demonstrate practical experience: caching, error handling, monitoring
- ✓Show you stay current: mention recent models (Claude 3.5, Llama 3, Gemini)
- ✓Connect to real projects: 'In my X project, I used Y because Z'
- ✓Prepare 2-3 specific examples of model selection decisions
🔄 Common Follow-up Questions
- →How do you handle LLM hallucinations in production?
- →Have you fine-tuned any LLMs? Why or why not?
- →How do you evaluate LLM output quality?
- →What's your experience with prompt engineering?
- →Have you built RAG systems? Which LLM did you use?
- →How do you manage LLM API costs in production?
- →What's your take on open-source vs commercial LLMs?
- →How do you handle rate limits and API failures?
- →Have you used LLMs with function calling or tool use?
- →What's the most challenging LLM integration you've done?
🎤 Practice Your Answer
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Target: 2-3 minutes
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