Language models have a fundamental problem for business use: they know a great deal about the world but nothing about your company. They don’t know your current rates, they don’t know your product catalogue, they have no access to your client contracts or your company policies. If you ask a GPT-4-based chatbot about your service prices, it will either make something plausible up or say it doesn’t know.
RAG (Retrieval Augmented Generation) solves that problem. Instead of being limited to the model’s training knowledge, the system first searches your own documents and data for relevant information, includes it in the conversation context, and the model responds based on that real, up-to-date information.
How RAG works technically
The process has three steps:
Indexing: The company’s documents (pricing PDFs, website pages, knowledge base articles, standard contracts, product sheets) are processed, split into fragments and converted into mathematical vectors that represent their semantic meaning. These vectors are stored in a vector database (Pinecone, Weaviate, Chroma or pgvector in PostgreSQL).
Retrieval: When the user asks a question, the question is also converted into a vector and the most semantically similar document chunks are retrieved. It is not an exact keyword search: if the user asks "how much does the chatbot cost?", the system retrieves the document chunks that discuss chatbot pricing even if they do not use exactly those words.
Generation: The retrieved fragments are included in the prompt to the language model alongside the user’s question. The model generates the response based on that specific information, not on its general knowledge.
The result: a chatbot that can answer specific questions about your company with up-to-date information, without hallucinations, and that can cite the source of each answer.
Use cases where RAG has the most impact
Customer service chatbot with its own knowledge: The chatbot can answer questions about returns policies, product availability, current prices and service terms. When information changes, the document in the knowledge base is updated and the chatbot automatically uses the new information without any reprogramming.
Internal company assistant: Employees can ask questions in natural language about company policies, HR processes, technical documentation or project history. Instead of searching through folders or asking colleagues, they ask the assistant.
Sales agent with catalogue knowledge: A voice agent or chatbot that can answer detailed questions about the products or services in the catalogue, compare options, explain technical differences and make recommendations based on the customer’s needs.
Legal document and contract analysis: An agent with RAG across all company contracts can answer questions such as "which contracts expire in the next 90 days?" or "what are the terms of the penalty clause in the contract with client X?".
What RAG doesn’t solve
RAG solves the problem of access to static or semi-structured information. It does not solve the problem of access to real-time data that changes constantly (current stock, real-time prices, the status of an order right now). For that, the agent needs calls to the APIs of the transactional systems, not RAG.
Also, the quality of the RAG depends on the quality of the documents that are indexed. If the company’s documentation is outdated, incomplete or inconsistent, the agent will give outdated, incomplete or inconsistent responses. GIGO: garbage in, garbage out.
Our conversational Chatbots implement RAG on each client’s documentation as part of the standard configuration process.