Explainers
Short definitions, useful analogies, and source-backed briefings for the AI concepts that affect work and learning.
An AI system that can plan steps, use tools, and work toward a goal with less step-by-step prompting from a person. A useful agent still needs permissions, constraints, monitoring, and a clear definition of success.
A structured test used to compare how well AI systems perform on a task. Benchmarks are useful for comparison, but they can become stale, overfit, or disconnected from real user workflows.
A public launch or update of an AI model, often with new capabilities, pricing, safety notes, or developer access. The important question is not only what the model can do, but who can use it and under what limits.
The gap between when the original source was published and when The AI Tea published its explanation. News posts must use sources no older than 14 days so readers are not handed stale AI updates as if they were new.
The original announcement, paper, documentation, report, or policy page behind a story, rather than a secondhand summary. Primary sources make it easier to verify claims and catch missing context.
Retrieval-augmented generation: a method where an AI system looks up documents or data before answering, so the response can be grounded in specific sources instead of relying only on model memory.
A model whose trained weights are publicly available under a license, allowing developers to run, inspect, or adapt it more directly than a closed API model. Open weights do not automatically mean unrestricted use.
A process for testing an AI system’s capability, safety, reliability, or limitations before or after release. Good evaluations explain the task, the limits, and what result would count as failure.
When an AI system calls external tools such as search, code execution, databases, calendars, or APIs. Tool use can make AI more useful, but it also creates permission, security, and reliability questions.
The amount of text, files, or conversation history an AI model can consider at once. Larger context windows can help with long documents, but they do not guarantee better reasoning or perfect memory.
A confident AI answer that is wrong, unsupported, or invented. Source links, retrieval, evaluations, and human review can reduce the risk, but not eliminate it entirely.
A rule, model, workflow, or product limit designed to reduce unsafe or unwanted AI behavior. Guardrails are useful signals, but they need testing because attackers and edge cases can bypass weak ones.
The stage when an AI model is actually being used to produce an answer, image, audio clip, prediction, or action. Inference cost, speed, and reliability matter for real products.
Additional training that adapts a model for a narrower task, style, or domain. Fine-tuning can help consistency, but it does not replace source grounding, evaluation, or careful data handling.
Rules, standards, laws, procurement requirements, or public commitments that shape how AI systems are built, released, purchased, and monitored.
Need a path?
Use the Start Here guide to learn how to read an AI claim, check the source, and decide whether it deserves your attention.
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