Generative AI & Prompt Engineering

Foundational models have changed the way many artificial intelligence applications are built. Instead of creating a separate AI model from scratch for every task, developers can start with a broadly trained model and adapt it for different uses. So, what is a common real-world application of foundational models? One of the most common applications is

A good prompt can make the difference between a vague AI response and an answer that is actually useful. Whether you are using ChatGPT, Copilot, Gemini, or another generative AI tool, the way you describe your request affects how well the system can respond. So, what are the three things that make a great prompt?

When people start learning prompt engineering, they often come across frameworks that help them write clearer instructions for AI tools. One of the better-known structures is the CO-STAR framework, which breaks a prompt into six useful parts. So, what does C stand for in the COSTAR framework? The answer is Context. In CO-STAR, C stands

Generative artificial intelligence can write text, create images, generate code, summarize documents, and produce other types of content. But none of these abilities happen without the computing resources that power the AI system. So, what does compute refer to in the context of generative artificial intelligence? In simple terms, compute refers to the computational resources

If you have used ChatGPT, Copilot, Gemini, or another AI assistant, you may wonder how these systems become capable of generating useful and natural-sounding text. The answer begins during the training process. The main objective during the training of large language models is to learn patterns in language so the model can accurately predict the

Generative AI tools such as ChatGPT and Microsoft Copilot can support people with much more than simply answering questions. They can help users write and improve content, understand complicated topics, brainstorm ideas, analyze information, create code, summarize documents, and work through problems. The important point is that these tools are best viewed as AI assistants

AI can perform many tasks without being given a long list of examples. You can ask a model to translate a sentence, classify a review, summarize an article, or explain a concept, and it may produce a useful response immediately. But how does it know what to do when you have not shown it an

Generative artificial intelligence can create text, images, code, audio, video, and many other forms of content. But behind these impressive outputs is a combination of essential technologies working together. If one of these foundations is weak, the quality, reliability, or scalability of the entire system can suffer. So, what are the three foundational layers of