GENERATIVE-AI-ENGINEER-ASSOCIATE · Question #29
A Generative AI Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of…
The correct answer is A. Invite users to submit long, rather than concise, questions. A RAG-based vehicle Q&A chatbot requires three core components: an embedding model (to convert documents and queries into vector representations), a vector database (to store and retrieve those embeddings via semantic search), and a response-generating LLM (to synthesize…
Question
A Generative AI Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information. Which of the following components will NOT be useful in building such a chatbot?
Options
- AInvite users to submit long, rather than concise, questions
- BResponse-generating LLM
- CEmbedding model
- DVector database
How the community answered
(34 responses)- A94% (32)
- B3% (1)
- C3% (1)
Explanation
A RAG-based vehicle Q&A chatbot requires three core components: an embedding model (to convert documents and queries into vector representations), a vector database (to store and retrieve those embeddings via semantic search), and a response-generating LLM (to synthesize answers from retrieved context). Encouraging users to submit long questions is actively counterproductive - verbose, unfocused queries degrade semantic search quality because the embedding becomes diluted across multiple topics. Best practice is to guide users toward concise, specific questions to maximize retrieval precision. Therefore, option A is the component that is NOT useful.
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