LangChain Interview Questions for 3 Years Experience

LangGraph has emerged as the go-to framework for stateful agent workflows, replacing brittle LangChain chains in production. Interviewers want graph thinking — nodes, edges, conditional routing, and checkpointing — not just API familiarity. Essential for any LangChain shop hiring GenAI engineers.

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

**Discovery** Instrument legacy script to log state snapshots; draw FSM on whiteboard; identify hidden globals.

**Pattern** Planner node outputs list of work items → map workers process each (possibly subgraph) → reducer synthesizes executive summary or ranked list.

**Unit layer** Pure functions: conditional edges, state merge logic, parsers — no graph compile needed.

**Retry patterns** Tenacity-style retries inside node or dedicated retry wrapper node incrementing state.retry_count. Exponential backoff for transient MCP errors.

**Requirements** Durability across pod restarts, concurrent threads, query by thread_id, retention/compliance deletes, backup/restore.

**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.

**LangChain agents** Higher-level executors (ReAct, tool calling) with less boilerplate. Good for demos and simple loops. Harder to debug complex branching, human interrupts, and precise persistence semantics.

**Cycle design** Classic ReAct loop: agent → tools → agent. LangGraph makes cycle explicit vs hidden while True loops. Conditional edge from tools checks whether to continue.

**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.

**Mechanics** After node N runs, a routing function reads state — e.g., last tool call, confidence score, intent label — and returns one or more next nodes. LangGraph supports static maps from route keys to targets. This replaces fragile if/else scattered in agent code.

Hallucination in RAG systems is the #1 production failure mode cited in AI engineering interviews. Your interviewer wants a systematic debugging framework — not a list of buzzwords. Learn how to measure faithfulness, fix retrieval precision, and layer mitigations the way senior engineers at Databricks and Meta actually ship RAG.

Structured JSON output from LLMs is a production necessity at Stripe, Vercel, and every AI-native startup. Interviewers test your reliability engineering — schema enforcement, retry logic, streaming edge cases — not just 'use JSON mode.' Master the full production playbook.

OpenAI's model lineup changes fast. GPT-4 vs GPT-4o is a model selection question that tests whether you understand latency, cost, multimodal capabilities, and when reasoning depth matters. Critical for any role touching OpenAI APIs in production.

Constitutional AI is Anthropic's differentiator and a must-know for Claude-focused interviews. Go beyond the marketing — explain the self-critique training loop, how CAI compares to RLHF, and practical safety implications for production deployments.

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.

Claude Code is Anthropic's CLI coding agent designed for repository-level work: reading the codebase, editing multiple files, running tests/builds, and iterating until a task succeeds. Unlike autocomplete-first copilots that live inside the editor, Claude Code operates as an agent loop — observe → plan → act (edit/run tools) → verify.

Interviewers want a practical comparison, not marketing.

Codex CLI represents OpenAI's push into agentic coding outside the editor — similar category to Claude Code and other CLI agents. The core loop is: understand task → explore repo → edit → run commands → iterate.

A production AI coding agent typically includes:

MCP (Model Context Protocol) defines a clean separation:

Prompt engineering focuses on instruction wording. **Context engineering** focuses on the full state fed to the model each step: policies, repository maps, retrieved files, prior tool outputs, memories, and task specs.

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