We’ve all experienced AI hallucinations—those moments when a chatbot or AI assistant confidently provides an answer that is completely wrong. AI models rely on pre-trained data and often lack access to real-time, company-specific information. When forced to answer, they may fabricate responses rather than admit gaps in their knowledge.
RAG solves this problem by allowing AI to retrieve relevant, up-to-date data dynamically. Instead of relying solely on its training data, the AI can search for and incorporate information from proprietary sources like internal documentation, structured databases, or real-time updates.
Take, for example, a large retail chain implementing a customer service chatbot. A shopper asks, “What time does the store at Willow Plaza close?”
The chatbot, trained on general FAQs, lacks real-time access to store-specific details. Instead of admitting it doesn’t know, it provides an incorrect answer, frustrating the customer. With RAG, the chatbot could pull real-time data from the company’s store databases and location directories, ensuring an accurate, up-to-date response.
How RAG Works
Suppose you have a PDF manual for your company’s VPN policies. You know that the LLM doesn’t have access to this information, so you copy and paste relevant sections into a chatbot before asking your question. The AI would then provide an answer based on the additional context.
This is an augmented prompt—you are supplying the AI with both a question and relevant context. Instead of manually adding this context every time, RAG automates the retrieval process, expanding the prompt dynamically with relevant data.
Here’s what happens when a user submits a question:
- Retrieval: The AI searches a knowledge base (PDFs, ticketing logs, internal documents, etc.) for relevant context.
- Augmentation: The retrieved data is added to the user’s query, creating an expanded prompt.
- Generation: The AI processes the augmented prompt and provides a more accurate, informed response.
This method eliminates guesswork and ensures that AI answers are grounded in actual company knowledge.
How RAG Retrieves the Right Information
Finding the right data efficiently is critical. Instead of searching documents using traditional keyword matching, RAG leverages vector search to retrieve information based on meaning.
- Embedding Model: Each document is converted into a numeric vector, capturing its meaning.
- User Query: When a user asks a question, their query is also transformed into a vector.
- Similarity Matching: The system finds the most relevant documents by comparing vector similarity, ensuring that results are contextually relevant, not just keyword-based.
- Retrieval & Augmentation: The best matches are retrieved and added to the LLM’s input before generating a response.
This allows AI to find and use relevant knowledge even if the phrasing of the query doesn’t perfectly match the stored text.
Scaling Up: How RAG Handles Millions of Documents
With traditional AI models, searching through vast amounts of data can be slow and ineffective. RAG overcomes this by using vector databases, which allow for fast, efficient retrieval of relevant content—even across millions of records.
Vector databases are:
- Optimized for high-speed searches over large datasets
- Able to handle constantly updated information (new policies, system updates, etc.)
- Designed for permission-based access, ensuring secure and controlled retrieval of company data
This makes RAG a scalable solution for enterprises looking to integrate AI into IT support, customer service, legal compliance, and more.
Optimizing Search with Compute Power
Many companies want AI to be smarter, but the real challenge is making it more informed. RAG isn’t just an upgrade—it’s essential for businesses that rely on constantly evolving proprietary information. By enabling AI to access real-time knowledge, RAG transforms chatbots, IT help desks, and enterprise automation into reliable, intelligent systems.
However, deploying RAG at scale requires significant computing power. Processing vector search queries and managing large knowledge bases efficiently demands high-performance GPUs.
Massed Compute provides on-demand enterprise-grade GPUs and CPUs, ensuring flexibility and scalability for AI-driven applications like RAG. Whether fine-tuning retrieval pipelines or handling vast datasets, our high-performance infrastructure delivers fast, accurate results without costly overhead—powering the future of AI-driven automation.
