Generative AI Virtual Assistants and Search with RAG — Theoretical Exploration
As a technology enthusiast, I often find myself fascinated by AI's potential applications in transforming how we interact with information. However, one of the key challenges between us and that future is developing virtual assistants and search tools that provide relevant answers without compromising data security. Could we combine the strengths of Generative AI and Retrieval-Augmented Generation (RAG) to create a more powerful, secure, and contextually aware search and virtual assistant experience?
The Struggle with Virtual Assistants and Search
We’ve all been there — struggling to find the right answer on search engines, sifting through the top 10 results, or constantly rephrasing questions to virtual assistants. The complexity of search and virtual assistant relevancy presents a significant hurdle for users, wasting valuable time and resources. The solution seems to lie in pre-trained models, often requiring immense computing and human effort to maintain and adapt.
Generative AI and its Limitations
As an avid follower of Generative AI (particularly ChatGPT), I’ve always been aware of its limitations. Despite its immense potential, Generative AI models often lack context and struggle to secure sensitive information. Additionally, enterprises are generally wary of adopting Large Language Models (LLMs) due to complexity, privacy concerns, ongoing experimentation, and cost.
RAG (Retrieval augmented generation)
The RAG model, an advanced AI technique that combines retrieval and generative approaches, has the potential to address these issues. By breaking down the retrieval and generative processes, RAG retrieves relevant information from a repository of documents and then generates contextually appropriate responses. This powerful concept can transform Enterprise Search and Virtual Assistants into Generative AI applications.
Implementing RAG: Concept
To leverage the RAG model, we can integrate it into the API layer of our backend, creating a “Generative API layer.” Here’s how the process could work:
1. The user sends a request to the Generative application through a search or a chatbot.
2. The API layer integrates with the RAG model, which connects to the search via a custom-built retriever. The search returns context-oriented results.
3. The Generative app combines the query, search results, prompt, and user context to create a context-aware response. For example, a draft prompt could be: “Imagine you are an AI assistant. You will provide me with answers from the given context. If the answer cannot be judged, say exactly, ‘I am sorry, I cannot find the answer,’ if the context has the ‘chatbot’ attribute; otherwise, do not answer at all.”
4. The response is sent back to the user in an appropriate format for the channel.
Fine-tuning the RAG model allows it to meet the specific needs of Enterprise Search and Chatbot applications. However, it’s important to note that the LLM still has access to search results and user context, so using a domain-scoped LLM (like Azure OpenAI or AWS LLM) is recommended to ensure data security within enterprise boundaries.
The RAG model offers an innovative way to deploy secure, relevant, and personalized Generative AI search experiences and virtual assistant interactions for users. While this concept remains theoretical, the potential impact on accessing and engaging with information is truly exciting.
References:
