A pharmaceutical company is building an internal knowledge base chatbot. The chatbot must provide accurate answers to queries based on a collection of hundreds of internal research papers, clinical trial results, and drug information documents. The company requires a solution that can ingest these documents, create a searchable knowledge base, and allow the chatbot to retrieve and synthesize answers from this specific content. Which Azure AI service is best suited for this task?
- AAzure AI Search with Retrieval Augmented Generation (RAG) pattern
- BAzure AI Language Understanding (LUIS)
- CAzure AI Translator
- DAzure AI Language (Text Summarization)
Show answer & explanationAnswer & explanation
Correct answer: A. Azure AI Search with Retrieval Augmented Generation (RAG) pattern
For building a chatbot that answers queries from a specific knowledge base, Azure AI Search combined with the Retrieval Augmented Generation (RAG) pattern is ideal. Azure AI Search indexes the documents, and its semantic capabilities help retrieve relevant information. This retrieved information is then used to augment a generative model (like an LLM via Azure OpenAI) to synthesize accurate answers, preventing hallucinations and ensuring answers are grounded in the provided documents.
Why the other options are wrong
- B. LUIS identifies intents and entities, but it doesn't build a knowledge base or synthesize answers from documents.
- C. Azure AI Translator translates text, which is not the primary requirement for creating a knowledge base chatbot.
- D. Text Summarization condenses text, it doesn't create a searchable knowledge base or synthesize answers from documents.
Azure AI Search with RAG
A pattern that combines Azure AI Search for efficient information retrieval from a specific knowledge base with a generative model (often an LLM) to synthesize accurate and grounded answers. This helps chatbots provide precise responses based on proprietary data, reducing hallucinations.
- Azure AI Search indexes and retrieves relevant documents.
- Retrieved data 'augments' a generative model's prompt.
- Ensures answers are grounded in provided source material.
- Reduces LLM hallucinations for factual accuracy.
Memory trick: Search RAG: Find facts, then speak smart.