Michael Trestman

Leaders of AI projects today may face pressure to deliver quick results to decisively prove a return on investment in the technology. However, impactful and transformative forms of AI adoption require a strategic, measured and intentional approach. 

Nvidia has long dominated the market in compute hardware for AI with its graphics processing units (GPUs). However, the Spring 2024 launch of Cerebras Systems’ mature third-generation chip, based on their flagship wafer-scale engine technology, is shaking up the landscape by offering enterprises an innovative and competitive alternative.

The most transformative promise of AI has always been its potential for autonomy, to create systems that can act intelligently on their own without human supervision. However, this kind of "Agentic AI" has remained out of reach for most enterprise use cases, until now.

Prompt engineering, the discipline of crafting just the right input to a large language model (LLM) to get the desired response, is a critical new skill for the age of AI. It’s helpful for even casual users of conversational AI, but essential for builders of the next generation of AI-powered applications.

In the age of artificial intelligence, prompt engineering is an important new skill for harnessing the full potential of large language models (LLMs). This is the art of crafting complex inputs to extract relevant, useful outputs from AI models like ChatGPT. While many LLMs are designed to be friendly to non-technical users, and respond well to natural-sounding conversational prompts, advanced prompt engineering techniques offer another powerful level of control. These techniques are useful for individual users, and absolutely essential for developers seeking to build sophisticated AI-powered applications.