Revolutionizing Industries with Generative AI: Streamlining Data-Driven Workflows and Enhancing Structured Outputs

Overcoming the Challenges of Large Language Models in Generative AI

Leveraging Amazon Bedrock: Two Approaches to Generate Structured Outputs

1. Prompt Engineering: Crafting Precise Inputs for Consistent Responses

2. Tool Use with the Bedrock Converse API: Advanced Integration for Reliable JSON Outputs

Step-by-Step Guide to Implementing Customer Review Analysis

Step 1: Configuring Amazon Bedrock

Step 2: Defining JSON Schema for Structured Outputs

Step 3: Crafting Effective Prompts

Step 4: Integrating Real-World Input Data

Step 5: Invoking Amazon Bedrock to Generate Responses

Evaluating Performance: Testing Claude Models on Structured Response Generation

Conclusion: Choosing the Right Method for Structured Response Generation in Generative AI

About the Authors: Expertise in Cloud Computing and Generative AI Solutions

Unlocking Innovation with Generative AI: A Deep Dive into Structured Data Utilization via Amazon Bedrock

Generative AI is at the forefront of a technological revolution, reshaping industries through enhanced operational efficiency and unprecedented innovation. While textual interactions with AI have become commonplace, the true potential of Generative AI often lies in its ability to interface with structured data through APIs, databases, and data-driven workloads. This structured data is vital for enhancing conversational AI, ensuring that outputs are not only reliable but also actionable.

The Challenge of Structured Outputs

At the heart of this innovation lies a persistent challenge: the inherent unpredictability of Large Language Models (LLMs). Trained primarily on unstructured text, such as articles and websites, LLMs can struggle when required to produce consistent structured outputs, such as JSON. This lack of precision can complicate their integration into existing systems, posing barriers to effective API and database connectivity. Notably, different models exhibit varying abilities in managing structured responses.

This blog discusses how Amazon Bedrock, a managed service for accessing top AI models, can mitigate these challenges by employing two distinctive strategies:

1. Prompt Engineering: A straightforward technique for directing models toward structured outputs.
2. Tool Use via the Bedrock Converse API: An advanced method for ensuring consistent and reliable schema integration.

We’ll illustrate these strategies through a customer review analysis example to show how Bedrock can produce structured outputs, including sentiment scores, with simple Python code.

Building a Solution with Prompt Engineering

Step 1: Configure Bedrock

The first step in utilizing Amazon Bedrock involves setting up a Python client connection via the Boto3 SDK. Here’s a brief overview of the key parameters:

• REGION: Your specified AWS region for model execution.
• MODEL_ID: The specific Bedrock model to be utilized.
• TEMPERATURE: Dictates output randomness (higher values encourage creativity, lower values enhance precision).
• MAX_TOKENS: Controls output length, balancing cost-efficiency and data richness.

Step 2: Define the JSON Schema

Defining a schema is essential for maintaining data integrity and facilitating API integration. A well-structured JSON schema lays out how the model should format its outputs. For our customer review example, we can define a JSON schema that includes:

• reviewId: (string, max 50 chars)
• sentiment: (number, range -1 to 1)
• summary: (string, max 200 chars)

Step 3: Crafting the Prompt

To ensure the model generates consistent and accurate outputs, your prompt should be:

• Clear about the model’s objectives.
• Divided into manageable steps.
• Supported by explicit instructions and examples.
• Designed to handle missing or invalid data.

By following these principles, you can guide the LLM effectively.

Step 4: Integrate Input Data

For demonstration, we’ll include a review text within our Python code:

review_text = "This product exceeded my expectations!"

This hardcoded input simulates real-world data and should ideally be integrated dynamically in a production context.

Step 5: Call Bedrock

The final step involves constructing a structured request to Bedrock. By defining a body object that combines your schema, prompt, and input data, you ensure that the model receives clear directives. Once the setup is complete, invoking Bedrock will allow you to generate a structured JSON response.

Exploring Tool Use with the Bedrock Converse API

Following our exploration of Prompt Engineering, let’s examine how to generate structured responses using the Amazon Bedrock Converse API.

Advantages of Tool Use

The Converse API simplifies multi-turn conversations with LLMs and includes functionality for “Tool Use”, enabling the model to interact with external tools, such as APIs. This capability allows for schema integration directly in tool definitions, improving output consistency.

In our customer review scenario, your code might look like this:

tool_list = [{"toolName": "ReviewAnalyzer", "inputSchema": input_schema}]

The integration of well-defined tools ensures adherence to the desired output format, streamlining interaction.

Testing and Evaluating Models

To assess different foundation model performances, extensive testing of Anthropic’s Claude models was conducted. Evaluating 1,000 iterations per model, we compared prompt-based and tool-based approaches, focusing on their ability to deliver structured outputs under varying complexities.

The results revealed that all models successfully achieved over 93% accuracy, with Tool Use consistently outperforming the Prompt Engineering approach.

Final Thoughts

In conclusion, Amazon Bedrock offers powerful solutions for generating structured responses through both Prompt Engineering and Tool Use. While the former is versatile, the latter provides greater reliability and consistency for APIs.

To fully leverage the capabilities of generative AI, structured data integration is paramount. Start exploring Amazon Bedrock today to unlock its full potential in real-world applications.

About the Authors

Adam Nemeth is a Senior Solutions Architect at AWS, specializing in guiding financial clients through cloud computing transformations.

Dominic Searle is also a Senior Solutions Architect at AWS, focusing on helping clients leverage Generative AI within their technology strategies.

Harnessing structured data within Generative AI products can drive substantial innovations across industries. Explore the possibilities with Amazon Bedrock and start shaping the future today.