LangChain in Production: What Actually Works

Why prototypes break in production

The demo works because you control everything - the questions, the documents, the expected behavior. Production means users ask weird questions, documents have inconsistent formatting, and edge cases are the norm rather than the exception.

Here's what typically breaks:

• •RAG retrieval quality: Your vector search returns technically similar but semantically wrong chunks. The model hallucinates because the context is irrelevant.
• •Prompt fragility: Complex logic lives in giant prompts. One small change cascades into unexpected failures somewhere else.
• •Latency and cost: Every query makes multiple LLM calls. Response times are measured in seconds. Your API bill is terrifying.
• •Debugging blindness: Something failed. Was it retrieval? The prompt? The model? The output parser? You have no idea.

Building RAG that doesn't hallucinate

Most RAG tutorials skip the hard parts. They show you how to embed documents and query a vector database. That's maybe 10% of a production RAG system.

The real work is retrieval quality. If you retrieve the wrong chunks, the best model in the world will still give you wrong answers - just with more confidence.

1. Chunking strategy matters more than embedding model. How you split documents determines what gets retrieved. Naive splitting by character count creates chunks that break mid-sentence, mid-concept, mid-usefulness.
2. Hybrid search beats pure semantic. Vector similarity misses exact matches. Combine it with keyword search (BM25) for better precision.
3. Reranking filters the noise. Retrieve more than you need, then use a cross-encoder to pick the actually relevant results.
4. Metadata filtering is your friend. Don't search everything. Filter by document type, date, department - whatever context you have.

When you need LangGraph (and when you don't)

LangGraph is LangChain's answer to complex, stateful AI applications. Think workflows with branching, loops, human approval steps, and parallel execution.

It's powerful. It's also overkill for most use cases.

• •Simple Q&A over documents? You don't need LangGraph. A well-built RAG chain is fine.
• •Multi-step workflows with conditionals? Now we're talking. LangGraph shines when you have "if this, then that" logic.
• •Human-in-the-loop approvals? LangGraph's checkpointing and persistence make this straightforward.
• •Parallel tool execution? If your agent needs to do multiple things at once, LangGraph handles the orchestration.

What production hardening actually means

Getting something to work is step one. Getting it to work reliably, affordably, and observably is the rest of the project.

• •Observability: LangSmith or similar tracing. Every step logged, every decision recorded. When things break, you know exactly where and why.
• •Caching: Cache embeddings, cache LLM responses where appropriate. A smart caching layer can cut costs by 80%.
• •Error handling: LLMs fail. APIs timeout. Output parsing chokes on weird responses. Every failure mode needs a graceful fallback.
• •Rate limiting: Don't let one bad actor burn through your API budget. Implement per-user limits.
• •Version control: Pin your dependencies. Test against updates before deploying. LangChain moves fast; things break.

None of this is glamorous. It's the difference between a demo and a product.

Is this the right approach for you?

LangChain is great for certain things. It's not the answer to everything.

• •You have an AI prototype that needs hardening. Perfect fit. This is exactly what production work looks like.
• •You need to query complex document collections. RAG is the right pattern. The question is execution quality.
• •You need multi-step AI workflows. LangGraph provides the structure. We provide the reliability.

Not sure where to start? We help you choose the right approach:

• •Simple chat interface needed? We can build that fast without over-engineering.
• •No prototype yet? We help you start simple and scale up as needed.
• •Prefer managed services? We implement AWS Bedrock, OpenRouter, or direct API integrations.