What happens when a knowledge source is fed to a generative AI agent

When you feed your knowledge source into a generative AI-powered AI agent, the text of the knowledge source is intelligently broken down into “chunks” of text. This is the first step in a framework called Retrieval-Augmented Generation (RAG). RAG enables your LLM to access information beyond its original training data, such as your carefully crafted help center articles.

These chunks are then stored in a database that’s organized by semantic meaning. When a user message is sent to the AI agent, the meaning of that message is compared with the meaning of the chunks in the database to surface the best match. That information is then used by your AI agent (in accordance with its instructions, persona and tone of voice, and safety guardrails) to answer the user’s message.

Here’s how generative AI uses your help center to answer customer questions in more detail:

1. Breaking it down: Initially, your help center is imported and segmented into what we call "chunks." These chunks vary in size, tailored to capture both the length and the intrinsic meaning of your content.
2. Understanding through numbers: Each chunk then receives its unique numerical signature—a vector representing the semantic meaning of the chunk. Essentially, it’s translating your text into a mathematical language that the AI agent can understand and store efficiently in a vector database.
3. Matching wits: When a user poses a question to your AI agent, the system compares the semantic meaning of the question with these stored vectors to find the best match. This process ensures that the most relevant chunks are retrieved to provide precise and informed answers.
4. Et voilà: Finally, your AI agent uses the retrieved information to answer the user’s query, in accordance with its instructions, persona and tone of voice, and safety guardrails.

By understanding the main point here—that chunks are the basis of your AI agent's replies—you can better prepare your help center to be more compatible and effective when integrated into your generative AI agent.

And while LLMs and RAG are at the forefront of today's technological advancements, capturing well-deserved attention with their innovative capabilities, we don’t have Artificial General Intelligence (that is, AI that can carry out all tasks that a human can) just yet. So as you integrate generative AI into your workflow, remember that it draws its insights entirely from the text chunks created from your connected knowledge sources rather than browsing or carrying out research in the background.