GenAI & LLMs
How LLMs actually work, capabilities and limits, hallucinations, and the foundations everyone should know.
9 articles
Reasoning models and when to use them: the hype is ahead of the practice
Reasoning models like OpenAI's o3 and Google's Gemini 2.0 Flash Thinking have captured attention by visibly "thinking through" problems before answering. The consensus says to use them everywhere you need accuracy, but that prescription is wrong in ways that will cost you money and slow your teams down.
Aug 10, 2026Open vs closed AI models: why the obvious choice keeps being wrong
Most organizations pick their AI model deployment strategy based on a simple story: open source is flexible and cheap, closed APIs are powerful and fast. That story leaves out the parts that actually determine whether a deployment succeeds or fails.
Aug 8, 2026Reasoning models: a practical playbook for knowing when to use them
Not every task benefits from a reasoning model, and using one indiscriminately wastes time, money, and attention. This playbook gives you a concrete decision process for matching the right model type to the right problem.
Jul 30, 2026Multimodal AI at work: a practical playbook for text, image, voice, and video
Most professionals are still treating multimodal AI as a novelty rather than a daily workflow tool. This playbook shows you how to combine text, image, voice, and video capabilities into concrete business tasks, starting this week.
Jul 26, 2026Multimodal AI explained: what it means when a model can see, hear, and read at once
Multimodal AI lets a single model process text, images, audio, and video together rather than treating each as a separate problem. Understanding how that works, and where it breaks down, changes how you design AI-assisted workflows.
Jul 18, 2026What LLMs actually are, and why the technical details matter for business users
Most professionals using AI tools in 2026 are working with systems they only partially understand, and that gap has real costs. Knowing what large language models actually do, and where they break down, changes how you use them and how much you trust their output.
Jul 11, 2026Why LLMs still confabulate, and what you should actually do about it
Large language models can produce confident, well-formatted, completely wrong answers, and the problem is structural, not a bug waiting for a patch. Understanding why confabulation happens changes how you design workflows, evaluate outputs, and decide when not to use an LLM at all.
Jul 4, 2026What LLMs actually are, and why the architecture still matters in 2026
Most professionals using AI tools in 2026 have no idea what is actually happening inside them. Understanding the core mechanics of large language models does not require a PhD, and it changes how you use these systems productively.
Jun 27, 2026Why most professionals are using LLMs wrong, and what to do about it
Large language models are no longer a curiosity, they are infrastructure. But understanding how they actually work is the difference between a power user and an expensive button-clicker.