Common Interview Mistakes
Below are common mistakes candidates make in interviews, with exact, block level citations for each sentence. Every sentence cites the source paragraph id from the provided handbook. Using outdated terminology is a co...
Below are common mistakes candidates make in interviews, with exact, block level citations for each sentence. Every sentence cites the source paragraph id from the provided handbook. Using outdated terminology is a common mistake.[:cite[1]{ln=1}] Treating LangChain and LangGraph as the same thing is a common mistake.[:cite[1]{ln=1}] Overselling agents without discussing reliability (e.g., tracing, persistence, retries, guardrails, evaluation) is a common mistake.[:cite[1]{ln=1}] Focusing only on demos instead of production concerns is a common mistake.[:cite[1]{ln=1}] Confusing memory with retrieval is a common mistake.[:cite[1]{ln=1}] Assuming more autonomy (more agentic behavior) is always better is a common mistake.[:cite[1]{ln=1}] Confusing embeddings (representations) with vector stores (retrieval infrastructure) is a common mistake.[:cite[2]{ln=1}] Underestimating production failure modes in RAG systems (e.g., stale indexes, poor chunking, noisy metadata filters, weak ranking, missing citations, silent retrieval misses) is a common mistake.[:cite[3]{ln=1}] Not treating persistence, human in the loop, evaluation, and observability as production concerns is a common interview weakness to avoid.[:cite[4]{ln=1}] Not using a “defense in depth” approach to prevent tool abuse, runaway cost, or unsafe actions is a noted gap in strong answers.[:cite[5]{ln=1}] If you want, I can turn this into a short checklist you can rehearse before interviews (one line prompts you can memorize), with the same citations on each item.