LangChain Interview Questions for 10 Years Experience

Hard orchestration comparison — LangGraph agent graphs vs durable workflow engines.

**Graph structure** draft → auto_checks → legal_subgraph → manager_subgraph → publish; reject edges loop to draft with comments in state.

**Supervisor pattern** Central node decides next worker or FINISH based on plan and worker outputs — alternative to flat handoff messages.

**Serving pattern** Stateless API pods; thread state in checkpointer DB; long runs may use queue workers resuming checkpoints.

**State schema** ticket_id, customer_tier, messages, retrieved_docs, proposed_actions, sentiment, escalation_reason, resolution_code.

**Parallelism model** LangGraph can schedule independent nodes in same superstep concurrently — e.g., search three sources at once. Reducers combine outputs into unified state field.

**Why subgraphs** Parent graph orchestrates; subgraphs own domain logic — billing dispute flow, code review pipeline. Teams ship subgraph versions independently.

**Checkpoint model** Each superstep persists channel values, metadata (thread_id, checkpoint_id), and pending writes. Enables resume after crash, human interrupt, or async long jobs.

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.

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.

MCP multiplies agent power and attack surface.

Prompt injection is the #1 security interview topic for agents that read untrusted text.

Company-wide context is a platform product.

Repository configuration attacks target the files agents trust.

Governance is becoming the bottleneck — not model IQ.

Treat agent models/prompts/tools like dependencies.

Advanced scenario question on why LLMs hallucinate — next-token prediction vs fact verification, root causes, and production defenses asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

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.