Plain-language definitions of the AI terms operators actually run into — the models, the ways you steer them, and the plumbing that connects them to your data and tools.
Here is the shorthand, decoded. For how affiliates apply it, read AI for affiliate marketing, sharpen your instructions with prompt engineering, and build a repeatable process in AI content workflows.
| Term | What it means |
|---|---|
| LLM | Large Language Model — the AI that generates and understands text. |
| AI agent | An AI system that can take multi-step actions toward a goal, not just answer. |
| Inference | The model generating an output from an input. |
| Token | A chunk of text the model reads or writes; usage is measured in tokens. |
| Context window | How much text an AI can consider at once. |
| Term | What it means |
|---|---|
| Prompt | The instruction you give an AI. |
| System prompt | A standing instruction that sets the model's role and rules for a whole session. |
| Few-shot prompting | Giving the model a handful of examples so it copies the pattern. |
| Temperature | A setting that controls how random or predictable the output is. |
| Fine tuning | Further-training a model on specific data for a specific job. |
| Term | What it means |
|---|---|
| RAG | Retrieval-Augmented Generation — feeding a model your own documents at answer time so it stays grounded. |
| Embedding | A numeric representation of text that lets a system compare meaning. |
| Vector database | A store of embeddings used to find the most relevant chunks fast. |
| Hallucination | When a model states something confidently that is simply wrong or invented. |
| Grounding | Anchoring a model's answer to trusted source material to reduce errors. |
| Term | What it means |
|---|---|
| API | Application Programming Interface — how one program calls another, including AI models. |
| Function calling | Letting a model trigger a defined tool or action, not just return text. |
| Prompt chaining | Passing the output of one prompt into the next to build a workflow. |
| Workflow automation | Stringing steps together so a task runs with little or no manual work. |
| Guardrails | Rules and checks that keep AI output safe, on-brand and compliant. |
Where to go next: put the vocabulary to work in AI for media buyers, turn one-off answers into a repeatable process with building AI research systems and automating repetitive tasks, and stay honest about the failure modes in AI risks and limitations.
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