What problem does LangChain solve, and when should I use it instead of the raw API?

LangChain gives you a unified abstraction (swap LLMs easily), batteries-included components (RAG / Agent / Memory) and a wide integration catalogue. Simple API calls or full control? Use the raw SDK. Complex multi-component apps or fast prototypes? Use LangChain. Plenty of teams mix the two — LangChain to start, drop to raw SDK on the hot paths.

What is a Chain in LangChain, and how do I link prompt + LLM + parser?

Compose components with the LCEL pipe `|`. Example: `chain = prompt | llm | StrOutputParser()`, then `chain.invoke({"role": "translator", "input": "Hello"})`. ChatPromptTemplate holds the template (system + user), ChatOpenAI is the model, StrOutputParser turns the message into a plain string. For JSON, use PydanticOutputParser or JsonOutputParser instead.

How do I build RAG with LangChain — which components do I need?

Three pieces: a vectorstore (e.g. `Chroma.from_documents(documents=docs, embedding=OpenAIEmbeddings())`), a retriever (`vectorstore.as_retriever()`), and the LLM. Wire them with LCEL: retriever pulls chunks → prompt assembles context → LLM generates. In production, layer metadata filters, rerank and citation output on top — LangChain's ContextualCompressionRetriever already covers filtering.

How do I use LangChain Agents, and how do they differ from calling function-calling directly?

Use `create_openai_functions_agent(llm, tools, prompt)` with off-the-shelf tools (DuckDuckGoSearchRun etc.) or your own. The agent runs the execution loop, error handling and tool-result feedback for you. Raw function calling gives more freedom but you write the while-loop, timeouts, retries and tool-selection validation yourself. Agents are great for prototypes and mid-complexity workflows; for heavy orchestration step up to LangGraph.

How should I debug and monitor a LangChain project?

Turn on LangSmith (the sibling tracing/eval platform) — every chain step, token count, latency and retry shows up in trace. For local dev, beyond `chain.invoke()`, use `chain.stream()` and `chain.batch()` to verify streaming and batched paths. LCEL lets you invoke any sub-chain independently for triage. In production, log the chain `run_id` for correlation, and version-control prompt changes with Git.

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