In a recent LinkedIn post, Ashleigh Early provides a candid and often humorous breakdown of common artificial intelligence terms, aiming to demystify the jargon for those who might be hesitant to ask. Early, a recognized voice in business and technology, clarifies the distinctions between concepts like GPTs, Bots, Agents, and LLMs, offering practical examples and a touch of “light snark” to make the information accessible.
The post addresses several key AI components, starting with the definition of a GPT.
“A custom AI you configure to do a repeatable task the same way every time. You give instructions, examples, files, tone. Then you feed inputs and get structured outputs. Think research in, email out. Over and over.”
As Ashleigh Early explains, a GPT is designed for efficiency when a specific thinking task needs to be performed consistently. Early suggests that if a user finds themselves performing the same task repeatedly, it might be an ideal candidate for a custom GPT.
Distinguishing Between AI Agents and Simple Bots
A significant portion of Early’s post is dedicated to differentiating between more sophisticated AI “Agents” and basic “Bots.” According to Ashleigh Early, the latter operates on a simple, rigid logic tree.
“If X, then Y. No reasoning. No memory. No judgment. Pure logic tree. Common in spammy outbound and ‘thanks for your message, reply STOP to unsubscribe’ energy.”
Early implies that relying solely on bots for sales strategies is a questionable approach, urging a deeper conversation for those who do. In contrast, an Agent is presented as a more dynamic entity.
“An Agent is a system that can decide what to do next. It can access tools, check results, adjust, and keep going toward a goal,” Ashleigh Early writes. Examples provided include agents that manage CRMs and calendars based on deal stages or those that run background research.
Understanding the Core: LLMs and Constrained Models
Ashleigh Early also tackles the fundamental concept of Large Language Models (LLMs), the underlying technology powering many AI tools. She likens LLMs to the “brain” of AI systems, such as GPT-4, Claude, or Gemini, explaining that their function is to predict the next word based on vast datasets.
“When you judge an AI tool, you are often judging the LLM under the hood. Better brain, better reasoning,” Early notes, highlighting the direct correlation between the quality of the LLM and the AI’s overall performance.
Furthermore, Early clarifies the notion of a “Constrained model.” This involves limiting an LLM’s data access to specific sources, like a company’s website or internal documents, rather than allowing it to process broad training data or live web information. Ashleigh Early argues this approach is crucial when precision and accuracy are paramount over creative output, providing necessary “guardrails.”
Context and Automation: Projects, Workspaces, and Workflows
The post further dissects terms like “Project or Workspace,” which Early defines not as an intelligence component, but as an organizational “container” for conversations, files, and instructions. This context is vital for AI systems to maintain continuity and remember user interactions.
Finally, Ashleigh Early addresses “Workflow Automation,” describing it as trigger-based sequences (If A happens, do B, then C, then D). She points out that these are often marketed as AI but may not always involve true artificial intelligence, especially when AI is integrated into just one step of the process.
Through this clear and concise explanation, Ashleigh Early empowers her audience to better understand the evolving landscape of AI terminology, encouraging further questions and discussion.
📝 About This Content
This article is based on insights shared by Ashleigh Early on LinkedIn.
📅 Originally posted on February 17, 2026 | View original post on LinkedIn →