You've heard about powerful AI models like GPT-4, but how do you make them answer questions specific to *your* business or documents? The answer often lies in a technique called Retrieval-Augmented Generation, or RAG.
Beyond General Knowledge
Standard Large Language Models (LLMs) are trained on vast amounts of internet data. While knowledgeable, they don't know the specifics of your latest product manual, internal policies, or unique service offerings.
How RAG Works
RAG combines the power of LLMs with information retrieval:
- Indexing Your Data: When you provide documents, website content, or text data (your Knowledge Base), the RAG system processes and indexes it, often creating vector representations.
- Retrieval: When a user asks a question, the system first searches your indexed data for the most relevant passages or chunks of information related to the query.
- Augmentation: The retrieved information is then provided as context alongside the original question to the LLM.
- Generation: The LLM uses its general knowledge *plus* the specific context retrieved from your data to generate an accurate and relevant answer.
Benefits of RAG (Used by CreateMyChat.com):
- Accuracy: Answers are grounded in your specific information, reducing hallucinations.
- Relevance: Provides contextually appropriate responses.
- Up-to-Date Information: Allows chatbots to use current information from your updated documents.
- Customization: Enables highly tailored chatbot experiences.
CreateMyChat.com leverages RAG to allow you to easily train AI agents on your knowledge base, creating truly intelligent and helpful conversational partners.