IBM Technology explains Jev as a different kind of AI model built for fast, bounded decisions rather than generating text. Jev is positioned as a “System 1” model: give it information plus predefined choices, and it returns calibrated probabilities for those choices, while conventional LLMs remain better suited to writing and more involved reasoning. For knowledge workers, the useful distinction is that some AI tasks are not really generation problems at all—they are repeated judgement problems that may be handled faster and more cheaply by a specialised decision model.

This IBM Technology explainer compares predictive AI, which uses historical data to estimate what is likely to happen, with generative AI, which learns patterns in data to create new content such as text, images, or code. The useful distinction is not simply “old AI versus new AI,” but whether the task requires a forecast or classification versus generating something new. For knowledge workers, the practical lesson is to match the type of AI to the job rather than treating generative AI as the answer to every problem.

This practical discussion explains why AI tokens matter less as an abstract technical unit than as the basis of increasingly difficult-to-control AI costs, particularly when agents repeat steps, call tools, and generate large amounts of work. The useful shift is from trying to minimise every token to measuring what the spending achieves: successful, accepted tasks rather than activity alone. For knowledge workers and organisations, the lesson is to remove waste while preserving the experimentation that produces genuinely valuable uses of AI.

This short explainer is really about the next layer of AI work: not better prompts alone, and not just better context, but the harness around the model that decides how tasks are broken down, repeated, checked, and continued. The useful distinction is that prompt engineering controls how you ask, context engineering controls what the model can see, and harness engineering controls the working system around the agent. For knowledge workers, the practical lesson is that reliable AI work depends less on a single clever instruction and more on the surrounding loop: task state, checkpoints, verification, and human judgement.

A practical discussion of how non-coders can use AI to build real products without treating “vibe coding” as random prompting. The useful lesson is that AI-assisted building still depends on clear thinking: understanding the problem, planning before prompting, creating PRDs and Markdown context files, testing parallel prototypes, watching the agent’s work, and using taste to decide what is actually good enough.

A clear comparison of RAG and CAG, two ways to help AI models answer questions using knowledge they may not already contain. The useful distinction is simple: RAG retrieves relevant information when a question is asked, while CAG gives the model a prepared body of context before it answers. For knowledge workers, this helps explain why some AI systems can answer from fresh or private information, why context size matters, and why different knowledge workflows need different designs.

A clear explainer of context windows: the limited working space an AI model uses to read your prompt, conversation history, documents, and its own response. The useful idea is that AI does not “remember” everything in the human sense; it can only work with what fits into its current context. For knowledge workers, this explains why long conversations drift, why large documents can strain output quality, and why good AI work often depends on managing what information goes into the model.