A: Hey HRX,

Excellent and very important question about controlling chatbot responses!

Yes, absolutely! This is a core principle of how we design agents in AgenticFlow, especially for website chat and customer support use cases, using Retrieval-Augmented Generation (RAG).

Here's how you achieve this:

1. Provide Specific Knowledge:

Website Content: When you create an agent, you can provide URLs from your website. The agent will crawl and index this content.

Uploaded Files: You can (and should!) upload specific documents (PDFs, DOCX, TXT, etc.) directly to the Agent's Knowledge Base. This could be your FAQs, product manuals, company policies, etc.

Table Data: You can also upload structured data (CSVs/Excel) as a Table Dataset and enable "Knowledge" on it for the agent to query.

2. Instruct the Agent (System Prompt is Key!):

In your Agent's System Prompt (its main set of instructions), you explicitly tell it how to behave. For your use case, you would include instructions like:

"You are a customer support assistant for [Your Website/Company Name]."

"Your primary role is to answer questions ONLY based on the information provided in your knowledge base (the website content and uploaded documents you have been trained on)."

"Do NOT use your general knowledge or information from outside these provided sources."

"If you cannot find an answer to a question within your provided knowledge base, politely state that you don't have that specific information and direct the user to our contact page [link to contact page] or email us at [support email]."

3. How RAG Works:

When a user asks a question, the agent first performs a semantic search across its specific, provided knowledge base (the website content and uploaded files).

It retrieves the most relevant chunks of information from your content.

It then uses the underlying LLM (e.g., Gemini Flash, 4o-mini, or your BYOK model) to synthesize an answer based on that retrieved context.

By strongly instructing the agent in its system prompt to stick to the provided knowledge and defining a clear fallback (like referring to contact info), you can significantly restrict its answers to be relevant and accurate to your website/business, minimizing the chances of it "hallucinating" or providing generic, off-topic answers from its base LLM training.

This combination of a curated knowledge base (RAG) and precise system prompting is how you build a reliable and focused website chat agent.

Hope this helps!
— Sean