Generative AI is a subset of artificial intelligence that focuses on creating content. Unlike rule-based or retrieval-based systems, which pick from a set list of predefined responses, generative models can create answers, texts, or even images that were not in their training data. For chatbots, this means the ability to generate answers to questions or engage in conversations that are much more dynamic and flexible.
If you're using the Smartly.AI platform, implementing generative AI into your chatbot can make interactions feel more natural and human-like. Customers won't feel like they are talking to a machine that has limited capabilities. Instead, they'll experience a fluid conversation, where the bot understands context, can answer follow-up questions, and even display a semblance of personality.
Smartly.AI utilizes Retrieval-Augmented Generation (RAG) for its generative AI capabilities. This is a hybrid approach that combines the benefits of both retrieval-based and generative models. In a RAG setup:
User inputs a question or statement.
- The system first retrieves relevant content or "passages" from a pre-defined database.
- These passages are then fed into a generative model.
- The generative model takes this information to produce a contextual, coherent, and appropriate response.
- The chatbot outputs this generated response to the user.
- This hybrid approach enables bots to provide highly contextual and accurate answers while maintaining the flexibility of a generative model.
- Dynamic Conversations: Generative AI allows your bot to have more natural, flowing conversations with users.
- Scalability: Since the model can generate responses, you don't have to manually input all potential questions and answers, saving you time and effort in the long run.
- Context Awareness: Generative models can maintain context in a conversation, providing more relevant and accurate responses.
- Quick Setup: With RAG, the setup is streamlined. You only need to define the instructions and select the required data sources to get started.
- Cost: Deploying a RAG model can be resource-intensive, leading to increased computational costs.
- Quality Control: Because the bot can generate its own answers, there may be instances where it produces incorrect or inappropriate responses.
- Hallucinations: Generative models can sometimes produce "hallucinated" information, or data that wasn't in the original training set or database, leading to inaccurate or misleading responses.
While RAG models can mitigate the issue of "hallucinations," or the generation of inaccurate or misleading information, they do not entirely eliminate this problem. The topic of hallucinations in generative models is still an active area of research, with new and promising approaches emerging to address this challenge.
Updated about 1 month ago
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