Question 35
Scenario 4
A climate science professor is developing an AI-powered learning assistant called ClimateBot to help students understand complex climate modeling techniques and computational approaches to environmental data analysis.
Guidelines for ClimateBot:
- Engage students through thought-provoking questions
- Encourage independent problem-solving
- Guide students to discover solutions autonomously
- Provide conceptual insights, not direct answers
- Help students develop robust computational and analytical skills
Based on the above data, answer the given subquestions.
Assume that the ClimateBot, an advanced learning assistant for climate science students.Its primary mission is to foster critical thinking, scientific reasoning, and deep understanding of complex environmental systems by using a RAG (Retrieval-Augmented Generation) system. The RAG system works as follows:
1. Climate Data Storage: Comprehensive climate research materials (like reports, research papers and notes) are split into smaller chunks. Each chunk is vectorized using an embedding model.
2. Vector Database: The vectorized chunks are stored in a FAISS or Weaviate vector database. 3. Retriever: When a student asks a question, the system retrieves the most relevant content (based on semantic similarity) from the vector database.
4. LLM Contextualization: The retrieved content is added as context in the assistant's prompt, which is then used by the LLM (like GPT-4) to generate a personalized, context-aware response. Which of the following correctly describes the process flow of a RAG-based system for the IITM TDS Teaching Assistant?