Can Someone Explain These AI Terms in Human Language?

Recently, some of my teammates started asking me questions like:

“What exactly is MCP?” “Why does everyone suddenly talk about AI Agents?” “Is Plugin the same thing as MCP?” “What is RAG?” “Why does AI sometimes suddenly become stupid halfway through a conversation?”

And honestly, I understand the confusion.

A lot of AI terminology today is explained by highly technical people, for highly technical people.

But many IT BAs, Product Owners, Product Managers, and operations people are now being pushed into AI adoption whether they are technical or not.

So this post is my attempt to explain the important AI terms using normal human language instead of engineering language.

The Core AI Terms You Actually Need to Understand

AI Agent

Think of an AI Agent as a junior digital worker.

Normal chatbot:

• answers questions

AI Agent:

• performs tasks
• follows workflows
• executes multi-step actions

Example:

• summarize meeting notes
• generate PRD draft
• analyze feedback
• create user stories
• compare competitor features

The important thing: Agents are not magically intelligent.

Most agents are simply:

• LLM + Instructions + Tools + Memory

Good process design still matters heavily.

LLM (Large Language Model)

This is the actual “brain.”

Examples:

• GPT
• Claude
• Gemini

The model itself does not know:

• your company
• your workflow
• your business logic
• your domain terminology

unless you provide it.

This misunderstanding creates many failed AI adoption projects.

People expect “smart AI.” But feed:

• messy documentation
• inconsistent business logic
• poor process definition

Then wonder why the output becomes unreliable.

Prompt

A prompt is simply instruction input.

Early AI adoption usually starts here.

Bad prompt:

Create user story.

Better prompt:

Create user story for Booking Confirmation page with validation rules, UI behavior, and API dependency assumptions.

AI quality is heavily affected by instruction quality.

This is why structured thinking suddenly becomes a competitive advantage for PMs.

Context Window

This is AI’s short-term working memory limit.

The larger the conversation becomes:

• the more information AI must remember
• the higher the chance important details get diluted

This is why long messy chats eventually degrade output quality.

For PMs, this is extremely important.

If your:

• meeting notes
• requirements
• process flows
• business rules

are unstructured, AI performance drops very quickly.

AI adoption is secretly forcing organizations to become more operationally structured.

Memory

Memory is different from Context Window.

Context Window:

• temporary conversation memory

Memory:

• long-term retained information

Example:

• remembering your product domain
• remembering workflow preferences
• remembering project structure

Without memory, AI behaves like a new employee every session.

Plugin

This term became popular during the early AI boom.

Think of Plugins like:

AI extensions for a specific platform.

Examples:

• Google Drive Plugin
• Jira Plugin
• Figma Plugin

Plugins usually:

• connect AI to one specific tool
• expose a limited set of actions
• are controlled by that platform integration

Simple mental model:

Plugin = custom integration for one platform

MCP (Model Context Protocol)

MCP actually came before the modern AI Plugin trend.

But recently, MCP became popular again because people realized building isolated plugins everywhere does not scale well.

Simplified explanation:

MCP is a standardized communication layer between AI and external tools/data.

Instead of building:

• separate Jira plugin
• separate Drive plugin
• separate internal system plugin

MCP tries to create one common way for AI to:

• discover tools
• fetch context
• execute actions

Simple mental model:

Plugin = one custom cable
MCP = universal USB standard

This is why many people believe MCP is important for enterprise AI adoption.

Large companies already have:

• ERP systems
• internal portals
• APIs
• databases
• documents everywhere

Without standardization, AI integration becomes operational chaos very quickly.

Skills

Skills are reusable AI workflows.

Example:

• PRD generation skill
• Meeting summary skill
• UAT generation skill
• Release note generation skill

This is where AI adoption becomes operational maturity.

Beginners use random prompts. Advanced teams standardize reusable skills.

This is also where many PM teams will evolve eventually.

RAG (Retrieval-Augmented Generation)

One of the most important concepts in enterprise AI.

Simple explanation:

AI temporarily looks up external information before answering.

Example: Instead of permanently teaching AI your company knowledge:

• AI searches company documents
• retrieves relevant information
• uses that information to answer

Think of it like:

Open-book exam mode

This is why:

• documentation quality
• structured knowledge
• organized files

suddenly become extremely important during AI adoption.

Bad knowledge structure = bad AI answers.

Fine-Tuning

This is different from RAG.

RAG:

• temporarily fetches information

Fine-tuning:

• permanently trains the model behavior

Simple explanation:

Fine-tuning changes how the AI itself behaves.

Example: You fine-tune AI to:

• answer in company tone
• understand shipping terminology
• follow internal writing style
• behave consistently for a specific domain

Think of it like:

RAG:

giving AI reference documents

Fine-tuning:

sending AI to long-term training school

Hallucination

One dangerous AI term everyone should know.

Hallucination means:

AI confidently gives wrong information.

This is why AI can sometimes:

• invent APIs
• fake business logic
• generate incorrect requirements
• produce non-existent references

The scary part: It often sounds extremely confident.

For PMs, this means:

AI output should be reviewed like junior staff work, not treated as absolute truth.

My Suggested Learning Path for Non-Technical PMs

Stage 1 — Personal Productivity

Start small:

• summarize meetings
• draft release notes
• improve stakeholder communication
• create requirement drafts

Do not jump into “AI transformation strategy” immediately.

Stage 2 — Structured Thinking

Learn:

• structured prompting
• reusable templates
• workflow standardization

This is the hidden PM advantage in AI adoption.

Stage 3 — Tool Integration

Connect AI into:

• Google Drive
• Jira
• Confluence
• Figma

Now AI becomes part of operational workflow instead of isolated chat usage.

Stage 4 — Knowledge & Data Awareness

This is where many companies struggle.

AI quality depends heavily on:

• structured data
• organized documentation
• clean process flow
• standardized terminology

AI does not magically fix operational chaos.

In many cases, AI simply exposes it faster.

Stage 5 — AI-Native Working Style

Eventually, the shift becomes cultural.

You stop thinking:

“How do I use ChatGPT?”

And start thinking:

“Which parts of my workflow should be AI-assisted?”

That is the real AI adoption journey.