👋 Goodbye AI Hallucinations! 🙅♀️
Ever get frustrated when your chatbot spits out irrelevant answers? This guide explores Agentic RAG, a game-changing approach to building AI that learns from its mistakes.
🧰 How it Works: Building a Self-Correcting Chatbot
- Knowledge Base: Imagine a treasure chest 🧰 filled with information about your business – that’s your knowledge base!
- Retrieval Agent: This clever agent acts like a librarian 📚, searching your knowledge base for answers to user questions.
- Relevance Checker: Think of this agent as a judge 👨⚖️. It analyzes the retrieved information and decides if it’s truly relevant to the user’s question.
- Generate or Rewrite:
- Relevant Answer? The generate agent crafts a helpful response 📝 based on the retrieved info.
- Irrelevant Answer? The rewrite agent steps in like a wordsmith ✍️, rephrasing the user’s question to get a better result.
- Continuous Learning: This entire process repeats, helping your chatbot learn and improve its responses over time. 📈
💡 Real-World Example: Oak & Barrel Restaurant
Let’s say a user asks Oak & Barrel’s chatbot: “What are your current specials?” The chatbot retrieves info about specials from the knowledge base. The relevance checker gives it a thumbs up 👍, and the generate agent provides the answer!
But what if the user asks: “Do you sell crypto?” 🤔 The chatbot searches its knowledge base, but the relevance checker spots a problem – the retrieved info isn’t relevant! The rewrite agent jumps in, rephrasing the question to something like, “Does Oak & Barrel accept cryptocurrency as payment?” Now, the chatbot can give a more accurate response.
🚀 Key Takeaway
By using Agentic RAG, you can create a chatbot that continually learns and improves, leading to happier users and a more efficient business.
🧰 Toolbox:
- Flowise Cloud: A powerful platform for building and managing AI agents.
- https://flowiseai.com/auth/signup?referralCode=LEONVZ
- Cognaitiv AI: Get expert help with building your chatbot.
- https://www.cognaitiv.ai
