Stop letting LLMs lie to your face with RAG

GPT-4o and Claude 3.5 Sonnet are absolute liabilities if you don't constrain them, because they'll confidently hallucinate your Q3 revenue numbers rather than admitting they're clueless. Relying on base model weights for private business intelligence is a fast way to burn cash on bad decisions. You need to treat the LLM like a student taking an open-book exam via Retrieval-Augmented Generation (RAG) instead of letting it guess from memory. I was messing around with a LangChain pipeline recently and realized the real overhead isn't the model cost—it's the technical debt you incur if your chunking strategy is garbage. If your vector database is filled with broken context because your RecursiveCharacterTextSplitter is too aggressive, your retrieval quality is going to tank and you're just throwing money at a broken system.

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import PDFLoader
loader = PDFLoader("company_docs.pdf")
docs = loader.load()
embeddings = OpenAIEmbeddings()
vector_store = FAISS.from_documents(docs, embeddings)

I've learned the hard way that a basic similarity search is amateur hour if you're moving toward actual production. You need a hybrid search—mixing semantic search with BM25 keyword search—to actually hit specific queries, and you absolutely need a reranking stage so you aren't feeding a mountain of noise into the prompt. You want to retrieve a bunch of docs but only pass the top-tier stuff to the LLM to keep token costs from spiraling.

# Retrieve many, rank few
retrieved = retriever.get_relevant_documents(query) # Get 100
ranked = rerank(retrieved, query, top_k=5) # Keep 5 best
context = "\n".join([d.page_content for d in ranked])

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
qa = RetrievalQA.from_chain_type(
 llm=OpenAI(),
 chain_type="stuff",
 retriever=retriever
)
result = qa.run("What's our Q3 revenue?")
print(result) # Now grounded in real data!

If you're looking for a place to host embeddings without overcomplicating your stack, check out:

Pinecone
Weaviate
FAISS
Milvus

It’s a lot of moving parts and high architectural complexity, but it's the only way to build an agent that actually provides value instead of just being a glorified, expensive chatbot. Check out promptcube3.com if you want to see how this scales.