Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

This blog post discusses two methods to generate the first draft of an earnings call script using large language models (LLMs): few-shot prompt engineering and fine-tuning. It evaluates the generated scripts and applied methods across different factors like comprehensiveness, hallucinations, writing style, ease of use, and cost.

Specifically, the article covers:

• Two methods for generating earnings call scripts using LLMs:
  • Few-shot prompt engineering with Anthropic Claude 3 Sonnet
  • Fine-tuning Meta Llama 2 70B with past earnings call data
• Solution overview and workflow for both few-shot and fine-tuning methods
• Evaluation of the generated scripts from both methods, based on:
  • Human reviewer assessment
  • Comparison of three variations using an LLM
• Conclusion highlighting trade-offs between the two methods and future potential directions