Glossary terms to avoid faster mediocrity with AI
This glossary is based on the Beyond Prompts: How to Avoid “Faster Mediocrity” with AI webinar, published on the AI Lab by ActiveCampaign.
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This is A scannable glossary of the AI and ActiveCampaign terms used in this webinar. Use it as a quick reference while you watch or while you work through the guides.
AI terms
Data lake:
- A company-wide pool of all the structured and unstructured data the organization holds, available to anyone with access to query it. Why it matters: most marketers don’t control it, but knowing it exists is how you ask for the slice you need.
Data pond:
- A subset of a data lake scoped to a division, country, or product line — narrower in scope and easier to act on than the full lake. Why it matters: a pond is what you ask your IT or data team for when the full lake is too broad to be useful.
Data puddle:
- A subset of a data lake scoped to a division, country, or product line — narrower in scope and easier to act on than the full lake. Why it matters: a pond is what you ask your IT or data team for when the full lake is too broad to be useful.
Faster mediocrity:
- Bob Pearson’s term for what happens when AI scales work that wasn’t strategically grounded in the first place — more output, same average quality. Why it matters: it names the failure mode most marketers hit in their first six months with AI and explains why the fix is upstream of the prompt.
Large language model (LLM):
- An AI system trained on huge volumes of text that generates language in response to prompts; it has access to public information but not your proprietary inputs unless you provide them. Why it matters: By default, an LLM will give you the average of the open internet — useful as a baseline, dangerous as a final answer.
Prompt supply chain:
- A series of strategic questions across the decision points inside a single task, instead of one prompt asking for the whole answer. Why it matters: it’s the technique that turns AI from a search box into a thinking partner.
Return on information (ROI):
- Bob Pearson’s reframe of return on investment for the AI era — measuring whether your decision precision improved and whether outcomes followed, not just whether you produced more output. Why it matters: it gives you a way to prove AI is helping your business, not just helping you ship.
Workspace:
- A persistent project inside an AI tool (ChatGPT projects, Claude projects, ActiveCampaign Active Intelligence) that retains context, files, and conversation history across turns. Why it matters: without a workspace, every prompt starts from zero and you lose the memory that makes a question chain compound.
ActiveCampaign terms
Active Intelligence:
- ActiveCampaign’s plain-language analytics layer that lets you ask questions of your CRM and campaign data in natural language. Where it lives: under Reports.
Automations:
- Sequences of triggers, conditions, and actions ActiveCampaign runs against contacts—the engine for turning a strategic question chain into a recurring workflow. Where it lives: under Automations.
Campaigns:
- Broadcast emails sent to a list or segment, often the channel where the four Cs framework’s content and channels intersect. Where it lives: under Campaigns.
Segments:
- Saved groups of contacts defined by behavior, attributes, or list membership — the building block for the customers C in the four Cs framework. Where it lives: under Lists → Segments.
Related
More data from the AI Lab.