You've heard terms like "RAG," "MCP," "fine-tuning," and "prompt engineering" thrown around in AI discussions. But what do they actually mean? And more importantly, why should you care as a manager?

This guide explains these concepts in plain English, with real-world examples that make sense for non-technical managers.

What is RAG? (Retrieval-Augmented Generation)

Layman Explanation

RAG is like giving AI a textbook before asking it questions.

Instead of relying only on what the AI was trained on (which could be outdated or incomplete), RAG searches your company documents first, finds relevant information, then generates an answer based on what it found.

❌ Without RAG

You:

"What's our company's vacation policy?"

AI:

"I don't know. I wasn't trained on your company data."

✅ With RAG

You:

"What's our company's vacation policy?"

[AI searches employee handbook, finds vacation policy section]

AI:

"According to your employee handbook (page 12), you get 15 days PTO per year, plus 10 holidays."

How RAG Works (Simplified)

1
Your question → AI converts to search query
"vacation policy" → search terms
2
Search your company database
Looks through wikis, docs, policies
3
Retrieve top 5 relevant documents
Finds employee handbook section
4
AI reads those documents
Understands context
5
AI generates answer citing those documents
Gives you accurate, sourced answer

When to Use RAG

•Company knowledge base: Wikis, internal docs, policies
•Customer support: FAQs, help articles, troubleshooting guides
•Legal/compliance: Regulations, policies, audit requirements
•Code documentation: Internal APIs, architecture docs

Tools that Use RAG

ChatGPT EnterpriseWith file upload

Notion AISearches your workspace

PerplexityWeb search + AI

GleanEnterprise search + AI

Cost

DIY RAG: $500-2,000 to build, $100-500/month to run

Off-the-shelf: $20-100/user/month

What is MCP? (Model Context Protocol)

Layman Explanation

MCP is like USB-C for AI tools—one standard that works everywhere.

Before USB-C, every device had a different charger. Now, one cable works for everything. MCP does that for AI tools—it creates a standard way for AI to talk to your tools (Slack, Jira, Google Docs, etc.).

❌ Without MCP

You have 10 AI tools and 5 integrations (Slack, Jira, GitHub, Google Docs, Notion)

10 × 5 = 50 custom connections

Nightmare to maintain, breaks constantly

✅ With MCP

AI tools speak MCP, your tools speak MCP

One standard connection

Easy to add new tools, reliable, consistent

What MCP Defines (Simplified)

1️⃣

How AI requests data from tools

"Get latest Jira tickets" → standard format

2️⃣

How tools send data back to AI

Jira → AI (standard format)

3️⃣

Authentication/permissions

Who can access what data

4️⃣

Error handling

What happens when things go wrong

When to Care About MCP

•Building custom AI apps that need to access multiple tools
•Want AI to access Slack, Jira, GitHub, Google Docs automatically
•Need consistent security/permissions across AI tools
•Evaluating AI vendors (ask: "Do you support MCP?")

MCP Status (as of 2025)

✓Anthropic launched MCP in late 2024
✓OpenAI, Google, Microsoft adopting
✓Becoming industry standard
✓Most new AI tools will support MCP by 2026

💡 PM Takeaway

When evaluating AI tools, ask: "Do you support MCP?" If yes, it will be easier to integrate with your existing stack and add new tools in the future.

Other AI Concepts You Should Know

Fine-Tuning

Layman explanation: Teaching AI your company's specific language and style.

Generic AI: "The bug is in the authentication module."

Fine-tuned AI: "The bug is in the AuthService class, likely the validateToken() method based on the error stack trace."

When to fine-tune: AI needs to understand your company jargon, you have 1,000+ examples

Cost: $500-2,000 to fine-tune, $200-500/month to use

Prompt Engineering

Layman explanation: Learning how to ask AI questions effectively.

❌ Bad Prompt

"Summarize this meeting."

✅ Good Prompt

"Summarize this meeting in 3 bullets: (1) Decisions made, (2) Action items with owners, (3) Open questions. Format as markdown."

5 Prompt Engineering Principles:

1. Be specific: Tell AI exactly what you want
2. Give examples: Show AI the format you want
3. Set constraints: Word count, tone, format
4. Iterate: Try 3-5 variations, pick best
5. Save winners: Build a prompt library

How HAIEC Uses These Concepts

🔍RAG for Compliance: HAIEC's compliance wizards search through SOC2, HIPAA, ISO27001 requirements to generate audit-ready evidence specific to your codebase.

🔌GitHub Integration: HAIEC connects directly to your GitHub repos (like MCP), automatically scanning for AI security issues on every commit.

🎯Deterministic, Not AI: Unlike other tools that use AI to "guess" compliance, HAIEC uses rule-based engines for zero-hallucination accuracy.

Explore HAIEC Platform