The Ins and Outs of Engineering AI Prompts

Large language models (LLMs) are powerful tools that require understanding and responsibility. A new discipline called prompt engineering is emerging, which involves understanding how LLMs learn and respond to inputs. LLMs process data by breaking it down into small units called tokens, which are used in both training and prompting. Prompt formatting is crucial for precision, and there are two basic forms of prompt engineering: Zero Shot and Few Shot. Zero Shot involves providing a specific format without prior responses, while Few Shot is a micro machine learning session that provides more context. Few Shot is useful for more complex tasks and can be incredibly helpful in enterprise use cases. The potential of prompts lies in their ability to create AI Agents, which can assist with tasks, provide consultation, and access external tools. AI Agents require great prompts to be engineered and consist of three core components: Planning, Memory, and a Toolkit. The possibilities of AI Agents are endless, and practicing prompts can lead to the creation of fun and powerful agents.