Tokenization Interview Questions for Freshers

Foundational model question on BPE/tokenizers — why token counts differ from words, and production impact on cost, limits, and RAG chunking.

Google's Gemini 1.5 Pro long-context window opens use cases impossible with standard LLMs — whole-codebase analysis, multi-hour video, massive document review. Interviewers test whether you understand real limitations behind the 1M token marketing number.

AGENTS.md is becoming the de facto 'README for agents.'

**Model choice** Flash for simple; Pro for hard multimodal only when needed.

**Use cases** Full codebase questions, hours-long media, multi-doc merger analysis.

**Strategies** Full dump only when eval proves benefit; otherwise RAG + selective long paste.

**Proxy layer** Backend holds keys; assigns budgets per customer/plan.

**Tracing** Span per node: inputs hash, duration, LLM/tool child spans. thread_id correlates user session.

**Stream modes** graph.stream() / astream_events yield incremental updates — node enter/exit, partial state, LLM tokens. Pick mode matching product needs: chat UI wants messages; ops dashboard wants updates.

**Interrupt API** LangGraph can interrupt execution before or after specified nodes — graph pauses, serializes checkpoint (lg-005), and returns control to host app. User sees proposed action; approver decision written to state; graph resumes from checkpoint.

Rate limiting for agents differs from human API usage — bursts are larger and less predictable.

Embedding similarity, k-NN example banks, MMR diversity, and eval-driven example curation for few-shot prompts.

Label definitions, calibration examples, abstain classes, and structured outputs for production classifiers without fine-tuning.

Medium AI Agents interview question on agent tracing and replay debugging — architecture, trade-offs, eval, and production patterns.

Medium AI Agents interview question on agent cost caps and budget controllers — architecture, trade-offs, eval, and production patterns.

Medium AI Agents interview question on stopping criteria and infinite loop prevention — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on end-to-end rag observability — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on cost of embeddings at 100m documents — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on compression and contextual distillation — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on agentic rag vs naive rag — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on hybrid search: bm25 + dense vectors explained — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on chunking strategies for different document types — architecture, trade-offs, eval, and production patterns.

Scenario question on cross-model tokenizer bugs — chunk boundaries, context overflow, and embedder vs LLM alignment.

Model question on log probabilities — uncertainty signals, calibration limits, and confidence workflows.