Generative AI refers to AI techniques that learn a representation of artifacts from data, and use it to generate brand-new, unique artifacts that resemble but don’t repeat the original data. These artifacts can serve benign or nefarious purposes. Generative AI can produce totally novel content (including text, images, video, audio, structures), computer code, synthetic data, workflows and models of physical objects. Generative AI also can be used in art, drug discovery or material design.
Source: https://www.gartner.com
Prompt Engineering is the discipline of providing inputs, in the form of text or images, to generative AI models to specify and confine the set of responses the model can produce. The inputs prompt a set that produces a desired outcome without updating the actual weights of the model (as done with fine-tuning). Prompt engineering is also referred to as "in-context learning," where examples are provided to further guide the model.
Source: https://www.gartner.com
Prompting Techniques
A Practical Guide to Building Agents (OpenAI)
Building Trusted AI in the Enterprise (Anthropic)
Claude Code: Best Practices for Agentic Coding (Anthropic)
Identifying and Scaling AI Use Cases (OpenAI)
Tracking AI - Monitoring Artificial Intelligence (A Maximum Truth Project)
