The Hallucination Problem

If you ask ChatGPT about your company's internal HR policy or a specific client's contract, it will either say "I don't know" or confidently invent a lie. This is called a hallucination, and it's the biggest barrier to enterprise AI adoption.

The solution isn't training a multi-million dollar custom model. The solution is RAG (Retrieval-Augmented Generation).

What is RAG?

RAG is a technique that connects an LLM (like GPT-4 or Claude) to your private data. When a user asks a question, the system first retrieves the relevant documents from your database, and then augments the LLM's prompt with those documents to generate an accurate answer.

The RAG Architecture

  1. Data Ingestion: We take your PDFs, Notion docs, and databases and chunk them into smaller pieces of text.
  2. Embedding: We convert these text chunks into mathematical vectors (numbers) that represent their semantic meaning using an embedding model (like OpenAI's text-embedding-3-small).
  3. Vector Database: We store these vectors in a specialized database like Pinecone, ChromaDB, or Supabase pgvector.
  4. Retrieval: When a user asks a query, we convert the query into a vector, find the "nearest neighbor" vectors in the database, and pull the original text.
  5. Generation: We send the user's query AND the retrieved text to the LLM with strict instructions: "Answer the question ONLY using the provided context."

Real-World Use Cases

At Krinok, we built InsurePrep, a RAG system for the insurance industry. It reads massive, complex insurance policy PDFs and allows agents to instantly query coverage limits and exclusions without reading 200 pages of legal jargon.

Cost to Build a RAG System in India

A production-ready RAG pipeline built by a specialized agency will cost between $5,000 and $12,000 depending on the complexity of the data connectors (e.g., syncing with live Jira boards vs. static PDFs).

Need a custom RAG system for your enterprise? Krinok can help.