The airline company aims to build a conversational AI assistant using large language models (LLMs) and a knowledge base to create a text-based chatbot with minimal development effort. Retrieval Augmented Generation (RAG) on Amazon Bedrock is an ideal solution because it combines LLMs with a knowledge base to provide accurate, contextually relevant responses without requiring extensive model training or custom development. RAG retrieves relevant information from a knowledge base and uses an LLM to generate responses, simplifying the development process.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Retrieval Augmented Generation (RAG) in Amazon Bedrock enables developers to build conversational AI applications by combining foundation models with external knowledge bases. This approach minimizes development effort by leveraging pre-trained models and integrating them with data sources, such as FAQs or databases, to provide accurate and contextually relevant responses."

(Source: AWS Bedrock User Guide, Retrieval Augmented Generation)

Detailed Explanation:

Option A: Train models on Amazon SageMaker Autopilot.SageMaker Autopilot is designed for automated machine learning (AutoML) tasks like classification or regression, not for building conversational AI with LLMs and knowledge bases. It requires significant data preparation and is not optimized for chatbot development, making it less suitable.

Option B: Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.This is the correct answer. RAG on Amazon Bedrock allows the company to use pre-trained LLMs and integrate them with a knowledge base (e.g., flight schedules or FAQs) to build a chatbot with minimal effort. It avoids the need for extensive training or coding, aligning with the requirement for least development effort.

Option C: Create a Python application by using Amazon Q Developer.While Amazon Q Developer can assist with code generation, building a chatbot from scratch in Python requires significant development effort, including integrating LLMs and a knowledge base manually, which is more complex than using RAG on Bedrock.

Option D: Fine-tune models on Amazon SageMaker Jumpstart.Fine-tuning models on SageMaker Jumpstart requires preparing training data and customizing LLMs, which involves more effort than using a pre-built RAG solution on Bedrock. This option is not the least effort-intensive.