Mike Stimson

Language Learning Models (LLMs) are rapidly evolving, becoming more sophisticated and versatile. To harness their full potential, crafting effective prompts is essential for optimal functionality. Crafting the best prompts is not an exact science. Different approaches can yield different results depending on the model and its training. Continuous training and fine-tuning are crucial for achieving the best outcomes. However, fine-tuning data isn’t always available.

Code quality is essential for long-term maintainability and adaptability of software. It involves trade-offs between aspects like complexity, readability, reliability, and performance. Prioritizing code quality from the start, despite potential sacrifices in run time or development speed, is crucial for future success.