Token Efficiency interview questions
Make LLM systems economical — prompt and context sizing, caching, batching, and the token/latency/cost trade-offs interviewers expect you to reason about.
33 questions23 free to practice10 company-verified (Pro)
Free to practice
- Vector Database Architecture — Conceptual Questions
- Token and Context Window Capacity Planning
- Embedding Quantization: Reducing Precision to Lower Storage Requirements
- Trim Chat Context to a Token Budget
- LRU Cache Final State
- LLM Cost Optimisation — Calculate and Reduce API Spend
- RAG System Token Cost Optimisation
- RAG System Retrieval Failure — Diagnosis and Fix
- Chunking Strategy Failure Analysis
- When should you use hosted APIs vs open-source models?
- When should you quantize a model and what are the tradeoffs?
- How do you reduce token usage in a high-volume LLM application?
- What is your chunking strategy — by length, semantics, or structure?
- How do you make LLM outputs deterministic and reliable?
- Few-shot vs zero-shot prompting — which works better where?
- How do embeddings work in large language models?
- Explain tokenization and how it affects LLM generation
- Multi-Modal Systems — Document Ingestion Strategy
- How would you make this AI system 60% cheaper without killing quality?
- Your embedding model changes — how do you migrate 50M vectors safely?
- What is your batching and caching strategy to reduce LLM latency?
- How do you choose a vector database for production RAG?
- LoRA vs QLoRA vs full fine-tuning — when to use each?
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- Hierarchical cache breakpoints: system prompt, tool definitions, context windows, query batches
- LLM Agent Tool Calls Exceed Context Limit Token Budget
- LLM RAG efficiency metrics latency throughput token usage cost analysis
- Optimise a RAG pipeline that is 3x over token budget
- Implement a RAG pipeline with citation tracking
- Fine-tuned model tokenizer mismatch with deployment environment configuration
- LoRA vs QLoRA vs Full Fine-Tuning for Mistral-7B Legal Domain Adaptation
- Enterprise RAG Pipeline: Scalable Search with Sub-200ms Latency Guarantees
- Insurance Claims Agent with RAG, Cost-Optimized LLM Integration
- Design a multi-tenant vector database for 10,000 enterprise customers
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