Connecting an LLM to a vector database isn't enough to build a RAG system that actually works. You need ingestion pipelines, chunking strategies, prompt engineering and error handling. LangChain gave us the foundations — and some headaches.
Why We Didn't Build Everything from Scratch
When we started RAG Enterprise PRO, the first impulse was to write everything in-house. "How hard can it be?" we thought. "Take the text, compute the embedding, put it in Qdrant, then ask the LLM." Simple in theory. In practice, a minefield. Problems emerged immediately. How do you split a 500-page PDF into chunks that maintain context? How do you handle tables, images, headers and footers? How do you format the LLM prompt so it cites sources correctly? LangChain doesn't solve all these problems automatically, but it gives you the right abstractions to solve them in a structured way. Document loaders for 20+ formats, text splitters with different strategies, prompt templates with variables. Building everything from scratch would have taken us 4-5 months. With LangChain, the working prototype arrived in 3 weeks.
Chunking: The Art of Cutting Documents
Chunking is probably the most underestimated phase of a RAG system, and the one that impacts answer quality the most. Chunks too small lose context. Chunks too large dilute information. We tested 4 strategies with LangChain on the Mueller Report (30 benchmark questions): 1. Fixed-size (500 tokens): 62% accurate answers 2. Recursive text splitter (500 tokens, 50 overlap): 71% 3. Semantic chunking (topic change): 78% 4. Our custom strategy (recursive + metadata enrichment): 91% Strategy #4 is what we use in production. For each chunk, we add metadata: chapter title, page number, document name, and a summary of the previous chunk. LangChain gave us the base (RecursiveCharacterTextSplitter), we added the enrichment layer on top.
Prompt Engineering: The Difference Between "Works" and "Works Well"
The prompt template is where our system goes from "generic answers" to "precise answers with citations". Our production prompt has 4 sections: system instructions, Qdrant retrieved context, user question, and output rules. A lesson learned the hard way: never tell the LLM to "answer exhaustively". We did that initially and the system fabricated details when chunks didn't contain enough info. Now the key rule is: "If the information is not present in the provided documents, explicitly state that you didn't find it." This single change reduced hallucinations from 15% to 2%. LangChain doesn't solve everything, and has the flaw of changing too often (between 0.1 and 0.3 we rewrote 30% of our code). But it remains the best choice for us: the most integrations, the largest community, and the most complete documentation.
FAQ About LangChain
Q: Is LangChain necessary to build a RAG system? A: No, you can do it without. But it's like building a house without power tools — it takes three times as long. LangChain accelerates development 3-5x. Q: Does LangChain work with local models like Ollama? A: Yes, perfectly. Just specify the Ollama provider and model name. We use LangChain + Ollama + Qdrant as a complete on-premise stack with zero cloud dependencies. Q: How much does LangChain cost? A: Completely open-source and free (MIT license). LangSmith, the monitoring tool, has optional paid plans. We only use the open-source library.
Related Services
See how we apply these technologies in our enterprise projects.
Interested?
Contact us to receive a personalized quote.
Securvita S.r.l. — i3k.eu