Enhanced Calendar Assistant
Project Overview
This project expanded upon a previous prototype of an LLM-powered calendar assistant, focusing on enhancing its capabilities through systematic application of prompt engineering techniques. I created an improved interface using React and Node.js, then implemented and evaluated various prompting strategies including self-consistency, chain-of-thought, few-shot, template, and persona approaches.
The research included a comprehensive analysis comparing these techniques across multiple state-of-the-art language models: GPT-4 Turbo, Mistral Large, GPT-3.5, and Mistral 7B. Results showed that combining few-shot learning with templating yielded the most significant performance improvements, with self-consistency also demonstrating strong standalone results.
A key technical contribution was the development of a simplified JSON-based calendar representation format that proved more effective than the previous ICAL approach. This research provides valuable insights into how prompt engineering can significantly enhance the performance of LLMs in structured tasks like calendar management.
Technologies
From complex ICAL to simple JSON structure
ICAL Format (Complex)
JSON Format (Simple)
Prompt Engineering Techniques
Click on a technique to see details:
Few-Shot + Template (Best)
Combined examples with structured format guidance.
Results & Conclusions
Performance across prompting techniques:
Key Findings
- Combined Few-Shot + Template approach was most effective
- Self-Consistency was the best standalone technique
- JSON format significantly improved model performance vs ICAL
- GPT-4 Turbo performed best overall