Lessons Learned After a Year of Building with Large Language Models (LLMs)

Over the past year, Large Language Models (LLMs) have reached impressive competence for real-world applications. Their performance continues to improve, and costs are decreasing, with a projected $200 billion investment in artificial intelligence by 2025. Accessibility through provider APIs has democratised access to these technologies, enabling ML engineers, scientists, and anyone to integrate intelligence into their products. However, despite the lowered entry barriers, creating effective products with LLMs remains a significant challenge. This is summary of the original paper of the same name by https://applied-llms.org/. Please refer to that documento for detailed information.

Fundamental Aspects of Working with LLMs

Prompting Techniques

Prompting is one of the most critical techniques when working with LLMs, and it is essential for prototyping new applications. Although often underestimated, correct prompt engineering can be highly effective.

• Fundamental Techniques: Use methods like n-shot prompts, in-context learning, and chain-of-thought to enhance response quality. N-shot prompts should be representative and varied, and chain-of-thought should be clear to reduce hallucinations and improve user…