Chunking Interview Questions for 10 Years Experience

Enterprise RAG interviews test system design at scale: ACL-aware retrieval, audit logging, and ingestion pipelines for millions of documents. This is a staff-level question appearing at Microsoft, Salesforce, and Fortune 500 AI teams. Walk through a complete architecture with security boundaries and operational concerns.

Part 2 of the LLM hallucination handbook — factual, citation, reasoning, math, code, temporal, context, and tool-use hallucinations; decoding risks; RAG failure modes; production case study.

Hard RAG ops question on index versioning, dual-write, and blue-green cutovers.

Shared embedding spaces, separate indexes, CLIP-style models, metadata routing, and fusion strategies.

Hard RAG interview question on late chunking and contextual retrieval — architecture, trade-offs, eval, and production patterns.

Hard context engineering trade-offs: prioritization tiers, dynamic reallocation, and what to drop first under pressure.

Hard RAG interview question on rag for codebases (repo qa) — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on graphrag vs vector rag — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on metadata filtering and acl-aware retrieval — architecture, trade-offs, eval, and production patterns.

Hard Microsoft company question — enterprise copilot grounding, Graph permissions, tenant isolation, and compliance.

Hard Google company question — document QA with OCR, layout, retrieval, multimodal models, and table/chart grounding.

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

**Fit** Nightly evals, backfill embeddings, document tagging — not interactive chat.

Field selection, normalization, ACL tags, temporal fields, and schema evolution without breaking filters.

Medium RAG interview question on parent-child and hierarchical chunking — 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.

Multi-agent orchestration is a senior/staff-level system design question gaining traction at Google DeepMind and Microsoft. Learn the supervisor pattern, shared state management, and how to avoid the 'too many agents' anti-pattern that sinks most candidate answers.

Self-hosting Llama 3 is a infrastructure-heavy question for ML platform and AI engineer roles at Meta-adjacent companies. Expect deep dives on quantization, vLLM, GPU sizing, and the TCO math that determines build vs buy decisions.

500M vectors at sub-100ms p99 is a staff-level vector search design question from Uber, Airbnb, and large-scale ML platform teams. Learn sharding strategies, index tuning, and the operational trade-offs that separate senior from principal engineers.

A production Claude Code rollout is an agent platform problem, not a 'give everyone a CLI' problem.

Cursor rules are context engineering in disguise.

Cost optimization for coding agents is context engineering + runtime policy.

This is an enterprise platform design question spanning agents, MCP, governance, and observability.