What is LangChain and why use it?
LangChain is a toolkit for building LLM apps that do more than answer a single prompt. Instead of hand-rolling the plumbing to fetch documents, store conversation history, call APIs, and chain steps together, you compose LangChain’s standard components. That portability — swap one vector store or model for another without rewriting your app — is the main reason teams adopt it.
How does retrieval-augmented generation (RAG) work?
RAG connects the model to your own knowledge. You split documents into chunks, convert each chunk to an embedding (a numeric vector), and store them in a vector database. At query time you embed the question, retrieve the most similar chunks, and pass them to the LLM as context. The model then answers from your data rather than guessing. Quality depends on sensible chunk sizes, a good embedding model, and tuning how many chunks you retrieve.
When should you use an agent instead of a chain?
Use a fixed chain when the steps are known in advance — summarize, then translate, then format. Reach for an agent when the model must decide what to do next: search the web, run a calculation, query a database, then synthesize. Agents are powerful but less predictable, so cap the number of steps, log every tool call, and add fallbacks when a tool fails.
What are the most common LangChain mistakes?
The biggest is skipping evaluation — shipping a RAG pipeline without measuring whether retrieved chunks actually answer the question. Others include over-chunking (tiny fragments lose context), ignoring token costs in long agent loops, and adding multi-agent complexity before a single agent works. Build the simplest thing that solves the task, measure it, then add components only when the data shows you need them.
How should you start with LangChain?
Build a minimal chain that calls an LLM with a prompt template. Add a retriever over a few of your own documents to make it a RAG app. Then give it one tool and turn it into an agent. Each step is a complete, useful app on its own, and the progression teaches the framework far faster than reading the full API surface.