Generate financial industry-specific insights using generative AI and in-context fine-tuning

This blog post demonstrates how to generate accurate and relevant analysis of tabular data using industry-specific language by providing large language models (LLMs) with in-context sample data in the prompt. This approach, called Generative Tabular Learning (GTL), allows LLMs to generate insightful analysis without the complexities of fine-tuning models.

Specifically, the article covers:

• Prerequisites for the demonstration, including access to LLMs like Meta's Llama models hosted on Amazon SageMaker or Amazon Bedrock, sample tabular datasets from the financial industry, and knowledge of prompt engineering techniques
• An overview of the GTL framework, which involves providing the LLM with data features, labels, sample data, and a sample analysis in the prompt
• Use case examples demonstrating how GTL prompts can improve the accuracy and relevance of LLM-generated analysis for complex financial industry questions
• Recommendations for building a curated set of GTL prompts and user questions to enable interactive applications that allow business users to gain insights from datasets using natural language
• Conclusion highlighting the potential benefits of GTL in generating industry-specific data analysis without the need for fine-tuning LLMs