22d • General discussion
Beginner Glossary of AI Terms
Beginner's Glossary of AI Terms We Throw Around
I know when you first start trying to learn AI stuff you start hearing everyone throwing around terms and statements and you have no idea what they mean. So I decided to post this here as a resource so that a new person can refer to it and know what we are talking about without feeling out of place. Feel free to ask us about any of these things because you will learn all these things in DBC.
1. AI Twin
A digital replica of you made with video or voice cloning tools like HeyGen.
It looks and sounds like you, reads a script, and appears on camera in your place, but it does not think or reason on its own.
2. Full-Stack AI Twin
A fully trained digital version of you powered by system prompts, memory files, and automations.
It can research, write, plan, and execute tasks across tools in a way that matches your style and decision patterns.
3. Prompt Engineering
The practice of designing clear, structured instructions for AI.
You define role, task, tone, purpose, output, and constraints so the model consistently gives you useful, on-brand results.
4. Vibe Coding
Building full applications using natural language prompts instead of writing code by hand.
You use platforms like Lovable or Bolt.new, describe what you want the app to do, and let AI generate the frontend, backend, and logic for you.
5. AI Agent
A semi-autonomous AI system that can plan, call tools or APIs, and complete multi-step tasks with minimal human input.
You give it a goal and it figures out the steps.
6. Automation Stack
The collection of tools, AI models, APIs, and workflows that you connect together to run processes automatically.
Think of it as your digital assembly line for repetitive work.
7. Context Window
The maximum amount of text or tokens a model can pay attention to at once.
If something is outside the context window, the model cannot see or use it when generating a response.
8. Retrieval-Augmented Generation (RAG)
A technique where the system searches your documents or databases first, then feeds the relevant chunks into the model before it answers.
This keeps responses grounded in your real data instead of the model guessing.
9. Chain-of-Thought
A step by step reasoning process where the model breaks a problem down before giving you a final answer.
You often trigger this by explicitly asking it to think through the steps.
10. Prompt Templates
Reusable prompt structures you can plug variables into.
They keep your outputs consistent across clients, tasks, and use cases while saving you time.
11. Temperature
A setting that controls how random or conservative the model’s answers are.
Low temperature gives predictable outputs. High temperature gives more creative and varied outputs.
12. API Call
A structured request from your app or automation to an external service like an AI model, CRM, or email system.
You send data in, get back a response, and use that response to continue the workflow.
13. Memory File
A curated document or set of notes that you repeatedly feed into the model to keep it aligned with your rules, offers, examples, and voice.
It acts like long term memory for your AI systems.
14. Workflow Automation
A sequence of actions that run automatically when a trigger event occurs.
For example, a new lead joins your list, AI writes a custom message, and your system sends it without you touching anything.
15. System Prompt
The hidden instruction layer that sets the identity and rules for the AI.
It tells the model who it is, what it should prioritize, and what it must avoid every time it runs.
16. XML Prompting
Structuring your prompts using tags like <role>, <task>, and <data> so the model can clearly separate instructions from content.
This reduces confusion and improves reliability for more complex workflows.
17. Agentic Loop
The cycle an AI agent follows when solving a problem.
It plans, takes an action, checks the result, adjusts, and repeats until the goal is met or it times out.
18. Embeddings
Vector representations of text that capture meaning.
They let your system search, compare, and cluster information by semantic similarity instead of simple keyword matching.
19. Latency
The delay between sending a request to the model and receiving a response.
Lower latency means snappier user experiences and faster automations.
20. Model Hallucination
When the AI generates confident but incorrect or fabricated information.
This usually happens when context is missing, the prompt is vague, or the model is pushed outside its knowledge.
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Beginner Glossary of AI Terms
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