AIF-C01 Dumps With Exact Questions and Answers

Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.

Adjust the Prompt:

Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.

A well-crafted prompt can provide specific instructions to the model, such as "Answer in a short sentence in Spanish."

Why Option A is Correct:

Control Over Output: Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.

Flexibility: Prompt engineering is a flexible approach to refining the model’s behavior without modifying the model itself.

Why Other Options are Incorrect:

B. Choose an LLM of a different size: The model size does not directly impact the response length or language.

C. Increase the temperature: Increases randomness in responses but does not ensure brevity or specific language.

D. Increase the Top K value: Affects diversity in model output but does not align directly with response length or language specificity.

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.

Prompt chaining is a technique where a complex task is broken into smaller subtasks, and the outputs of one subtask are used as inputs for the next, sequentially guiding a large language model (LLM) to solve the problem step-by-step. This method is particularly useful for complex tasks that require multiple reasoning steps.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Prompt chaining involves breaking a complex task into smaller subtasks and sequentially passing the output of one subtask as input to the next, enabling large language models to handle intricate problems by solving them step-by-step."

(Source: AWS Bedrock User Guide, Prompt Engineering Techniques)

Detailed Explanation:

Option A: One-shot promptingOne-shot prompting provides a single example to guide the LLM, but it does not break tasks into smaller subtasks or handle sequential processing.

Option B: Prompt chainingThis is the correct answer. Prompt chaining divides a complex task into smaller, manageable subtasks, solving them sequentially with the LLM, as described.

Option C: Tree of thoughtsTree of thoughts involves exploring multiple reasoning paths simultaneously, not breaking tasks into sequential subtasks.

Option D: Retrieval Augmented Generation (RAG)RAG retrieves external information to augment LLM responses but does not specifically break tasks into sequential subtasks.