RAG System Development: Retrieval-Augmented Generation for Business

RAG (Retrieval-Augmented Generation): Why Your Business Needs It and How to Implement

Your company has accumulated thousands of contracts, regulations, and instructions, yet employees spend hours searching for answers. Implementing Retrieval-Augmented Generation (RAG) solves this: the LLM queries your corporate knowledge base in real time and delivers answers with source citations. Our RAG architecture integrates semantic search with vector databases for efficient document indexing and retrieval, ensuring high faithfulness. Unlike fine-tuning, RAG requires no retraining — you simply update the documents, and the system immediately picks up changes. We have 5+ years of experience building RAG systems and have delivered dozens of projects for insurance, legal, and IT companies. Our solutions are trusted by industry leaders for their reliability and accuracy.

The time savings from RAG adoption can be up to 80% on information retrieval — for a typical enterprise, this translates to annual savings of over $100,000 in lost productivity. Get in touch for a consultation to evaluate the economic impact for your company.

We develop end-to-end RAG systems — from document indexing to integration with your services. Below is how it works and what's included.

What a RAG System Consists Of

User → Query ↓ Embedding Model ↓ Vector Search (Top-K) ↓ Retrieved Chunks + Query ↓ LLM ↓ Answer 

Components:

• Indexing pipeline: document loading, chunking, embedding, storage in vector DB.
• Retrieval: convert query to vector, nearest neighbor search.
• Generation: pass context + query to LLM.

When RAG Is More Effective Than Fine-Tuning

Fine-tuning requires a labeled dataset and model retraining — costly and time-consuming. RAG allows adding new documents without retraining: just place a file in a folder, and the index updates. For tasks where data changes weekly (contracts, documentation, knowledge bases), RAG is significantly cheaper and faster. Additionally, RAG provides traceable sources, critical for legal and medical scenarios.

Tech Stack for RAG Systems

Building the Indexing Pipeline

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Qdrant
from langchain_community.document_loaders import PyPDFDirectoryLoader

# Load documents
loader = PyPDFDirectoryLoader("./docs/")
documents = loader.load()

# Split into chunks
splitter = RecursiveCharacterTextSplitter(
    chunk_size=512,
    chunk_overlap=64,
    separators=["\n\n", "\n", ".", " "],
)
chunks = splitter.split_documents(documents)

# Embedding and save
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = Qdrant.from_documents(
    chunks,
    embeddings,
    url="http://localhost:6333",
    collection_name="corporate-docs",
    force_recreate=True,
)

Answering a Query

from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.prompts import ChatPromptTemplate

template = """You are an assistant that answers strictly based on the provided context. If the answer is not in the context, say "Information not found in the knowledge base." Always cite the source (document name and section) using <cite> tags, for example: <cite>Contract Section 4.2</cite>.
Context: {context}
Question: {question}
Answer:"""
prompt = ChatPromptTemplate.from_template(template)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

# Retrieval + Generation
retriever = vectorstore.as_retriever(
    search_type="mmr",  # Maximum Marginal Relevance — reduces duplication
    search_kwargs={"k": 5, "fetch_k": 20}
)
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    chain_type_kwargs={"prompt": prompt},
    return_source_documents=True,
)
result = qa_chain.invoke({"query": "What is the warranty period?"})

Practical Case: RAG for an Insurance Company (from Our Practice)

Challenge: an assistant for handling customer inquiries — searching insurance contracts, payout rules, precedent decisions (12,000 documents, ~2M pages).

Key decisions:

• Embedding: BGE-M3 (multilingual, works well on Russian, free self-hosted). Dimension 1024.
• Chunking: hybrid strategy — structural boundaries (contract sections) instead of fixed size. Chunk size 200–600 tokens.
• Reranking: CrossEncoder after vector search. Top-50 candidates → Top-5 after rerank. +18% faithfulness.

Metrics (RAGAS):

Self-hosted embedding models are significantly cheaper than OpenAI and provide comparable quality on Russian. This lowers the total cost of ownership of the RAG system — no per-API-call charges. Request a project cost estimate — contact us.

What Affects RAG Accuracy?

RAG system accuracy depends on several factors: chunk quality, embedding model choice, retrieval strategy, and presence of a reranker. Even small changes in chunk_size can shift metrics by 10–20%. Small chunks (128–256 tokens) yield high retrieval precision but may lack full context. Medium chunks (512–1024 tokens) strike a balance — optimal for most tasks. Large chunks (1024–2048 tokens) capture more context but degrade retrieval precision. For documents with long, interconnected sections, use Parent Document Retriever: index small chunks for retrieval, return large chunks to the LLM.

What's Included in RAG System Development?

1. Audit of existing data and requirements.
2. Technology stack selection and architecture design.
3. Indexing pipeline development (chunking, embedding, storage).
4. Retrieval tuning (vector search + reranker).
5. LLM integration with custom prompt and sources.
6. Quality metrics collection (RAGAS, manual validation).
7. Documentation, team training.
8. Post-launch support.

Timelines and Cost

• Prototype (basic RAG): 1–2 weeks. Starting at $15,000.
• Production-ready system with quality evaluation: 4–8 weeks.
• Extended RAG (hybrid search, reranking, evaluation): 8–14 weeks.

Cost is calculated individually. Get in touch for a 30-minute consultation on RAG implementation.