Prompt engineering is the process of crafting, testing, and refining AI prompts to ensure that LLMs generate reliable, high-quality, and contextually appropriate responses. In Future AGI, prompt engineering is structured around template management, execution tracking, optimization, and evaluation, providing a systematic way to improve prompt effectiveness over time.
A key feature of prompt engineering system in Future AGI is optimization, which systematically improves prompt performance through an iterative process.
Data Preparation: The system splits execution data into training and validation sets, preventing overfitting and ensuring prompts generalise well.
Mini-Batch Processing: Prompts are tested in small batches, allowing fine-tuned adjustments based on performance metrics.
Feedback Integration: The system analyses response patterns and refines prompt phrasing to increase clarity, reduce ambiguity, and enhance output consistency.
Parallel Processing: Optimizations are run in parallel to speed up improvements without sacrificing accuracy.
This approach allows Future AGI to iteratively enhance prompts, ensuring they remain effective across different datasets and AI models.