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Answer: Use Vertex AI Agent Builder to create an agent. Securely index the organization’s internal documentation to the agent’s datastore. Send users’ queries to the agent and return the agent’s grounded responses to the users.
Option C is the optimal choice because it directly addresses the requirements using Vertex AI Agent Builder, which is specifically designed for building conversational agents with Retrieval-Augmented Generation (RAG) capabilities. It allows quick ingestion of PDF documents into a datastore, provides grounded responses by retrieving relevant information from the documentation, and minimizes maintenance overhead through managed services. The community discussion strongly supports this approach, with 100% consensus on C, highlighting RAG as the easiest solution and noting practical implementation success. Option A involves unnecessary complexity with custom fine-tuning, which is time-consuming and requires ongoing model maintenance. Option B provides semantic search but lacks the conversational, answer-generation capability needed for an interactive tool. Option D is manual and reactive, requiring continuous updates based on feedback, which increases maintenance overhead and does not scale.
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Your organization's employee onboarding team needs an interactive self-help tool for new employees. The tool must accept user queries and return answers sourced from the company's internal documentation, which is distributed across standalone PDF files. You need to build a solution rapidly while keeping maintenance overhead to a minimum. What should you do?
A
Create a custom chatbot user interface hosted on App Engine. Use Vertex AI to fine-tune a Gemini model on the organization’s internal documentation. Send users’ queries to the fine-tuned model by using the custom chatbot and return the model’s responses to the users.
B
Deploy an internal website to a Google Kubernetes Engine (GKE) cluster. Build a search index by ingesting all of the organization’s internal documentation. Use Vertex AI Vector Search to implement a semantic search that retrieves results from the search index based on the query entered into the search box.
C
Use Vertex AI Agent Builder to create an agent. Securely index the organization’s internal documentation to the agent’s datastore. Send users’ queries to the agent and return the agent’s grounded responses to the users.
D
Deploy an internal website to a Google Kubernetes Engine (GKE) cluster. Organize the relevant internal documentation into sections. Collect user feedback on website content and store it in BigQuery. Request that the onboarding team regularly update the links based on user feedback.