LLMs & Prompt Engineering: The Complete Foundation
Start here. No prior knowledge needed. By the end, you'll understand exactly what LLMs are, what prompts do, and how they work together to build automation.
1. What is an LLM? start here
LLM = Large Language Model
Simple definition: An AI that has read millions of books, websites, and documents, and learned to predict and generate human-like text.
Think of it as an intern who has read everything on the internet. They know facts, writing styles, languages, and patterns - but they need clear instructions to do useful work.
🎯 Analogy: The Super-Read Intern
Imagine you hire an intern who has read:
- Every book ever written
- Every website, blog, and forum
- Every email and document (anonymized)
- Every code repository
That's an LLM. They know everything, but they don't know what you specifically need unless you tell them clearly.
Training Data
Billions of texts from the internet: Wikipedia, books, Reddit, academic papers, code, etc.
What It Learned
Language patterns, facts, reasoning, writing styles, translation, summarization, code generation
Real examples of LLMs: GPT-4 (ChatGPT), Claude, Llama, Gemini. They're the engine - prompts are the steering wheel.
2. What Can We Build? (Real Business Examples)
LLMs alone do nothing. Combined with prompts, they become tools that save businesses 10-40 hours per week.
Email Automation
Read incoming emails, draft personalized replies, categorize support tickets
Lead Qualification
Score leads 1-10 based on their inquiry, budget, timeline, and fit
Content Generation
Turn 1 blog post into 10 social posts, newsletters, and summaries
Support Bots
Answer FAQs, route complex issues to humans, suggest solutions
Data Extraction
Pull names, dates, amounts, and key info from messy documents
Code Generation
Write SQL queries, Python scripts, and automation code
The pattern: LLM + Clear Instructions (Prompt) = Business Automation
3. What is a Prompt? the instruction
Prompt = The instruction you give to the LLM
A prompt is text that tells the AI what you want it to do. It can be a question, a task, an instruction, or a template with blanks to fill in.
📋 Simple Examples:
Basic prompt: "Write an email to a client"
Better prompt: "Write a professional email to a client who hasn't paid their invoice in 30 days. Be firm but polite."
Advanced prompt: "You are a collections specialist. Write to client ABC Corp about invoice #1234 ($5,000) due 30 days ago. Offer a payment plan if needed. Max 150 words."
What can prompts include?
- ✅ Instructions (what to do)
- ✅ Examples (how to do it)
- ✅ Context (background info)
- ✅ Constraints (limits, rules)
- ✅ Format (how to structure output)
- ✅ Persona (who to act as)
4. What is Prompt Engineering? the skill
Prompt Engineering = The art and science of writing prompts that consistently get the desired output
It's not just "writing a question." It's designing instructions so that the AI produces reliable, predictable, and useful results every time.
🎯 Why it matters:
Same LLM + Different Prompts = Completely Different Results
Prompt engineering turns a generic AI into a specialized business tool. The same LLM can be a sales rep, support agent, analyst, or writer - just by changing the prompt.
5. The Connection: LLM + Prompt = Automation
LLM
The engine (knowledge + reasoning)
Prompt
The instructions (what to do)
Automation Tool
A system that does useful business work automatically
Real workflow example:
- Email arrives: "I'm interested in your software for my 50-person company"
- LLM receives prompt: "Score this lead 1-10. Consider company size, interest level, and keywords. Return JSON."
- LLM outputs: {"score": 8, "reason": "Mentioned company size, decision-maker tone"}
- Automation sends to sales team immediately
6. See Prompt Engineering in Action
Same task: Handle a customer complaint
Prompt: "Reply to this angry customer"
Output: "Thank you for your feedback. We're sorry you're unhappy. We'll do better."
Problem: Generic, doesn't solve anything, customer gets angrier
Prompt: "You're a senior customer support manager. The customer is angry because their order #12345 is 5 days late. Apologize sincerely, explain it's due to weather, offer 20% discount code, and provide tracking link. Keep tone empathetic but professional. Max 120 words."
Output: "Dear Mr. Smith, I'm genuinely sorry your order #12345 hasn't arrived. As a manager, I've checked personally - there's a weather delay in your region. I've added a 20% discount to your account for the trouble. Your package is now scheduled for tomorrow: [tracking]. Please reply directly if I can help further. - Sarah"
Result: Customer feels heard, problem addressed, relationship saved
P Persona
Assign a specific role with expertise level, background, and communication style.
R Role
Define what the AI needs to accomplish in concrete terms.
E Example
Provide 1-3 examples of the desired output format and style.
C Context
Give relevant background information and constraints.
I Intent
Explain why you need this and what action follows.
S Style
Specify tone, length, format, and key phrases to use/avoid.
I Input
Define exactly what data/variables you're providing.
O Output
Specify the exact structure (JSON, markdown, plain text).
See the Difference
Task: Draft an empathetic reply to a delayed order inquiry
Context: Order #12345 is delayed by 3 days due to weather. Customer is a VIP (5 years, 50+ orders)
Style: Warm, apologetic but confident, offer 20% discount code, max 120 words
Input: "My order hasn't arrived and it was supposed to be here yesterday"
Output: Professional email with subject line, greeting, apology, explanation, solution, and signature
Amateur Output
"Sorry for the delay. Your order will arrive soon. Thanks for your patience."
Precision Output
"Dear Michael, I'm personally sorry your order #12345 is delayed. As one of our most valued customers (5 years with us!), you deserve better. There's a weather delay in your area, but I've added a 20% courtesy credit to your account. Your items are scheduled for delivery tomorrow by 8pm. Track here: [link] -Sarah, Customer Success Lead"
How We Teach You to Write Like This
Start with the end in mind
Before writing anything, answer: What specific action will be taken based on this AI output?
Write the perfect output first
Manually write 2-3 examples of what you want the AI to produce. This becomes your "Examples" in the framework.
Build the prompt backwards
Using your manual example, fill in each PRECISION element:
Test and iterate
Run 10 different inputs. If any fail, add more examples or tighten constraints.
Your Turn: 3 Real Exercises
Exercise 1: Email Reply
Write a PRECISION prompt that generates professional replies to support tickets. Include:
- Company tone: friendly but efficient
- Must ask for order number if missing
- Max 100 words
- 3 variations of responses
⏱️ 12 min
Exercise 2: Lead Scoring
Build a prompt that scores leads based on:
- Budget mention ($ vs $$$)
- Timeline (urgent vs exploratory)
- Authority (decision-maker vs researcher)
- Return JSON with score 1-10
⏱️ 15 min
Exercise 3: Content Repurposing
Create a prompt that turns blog posts into:
- 5 LinkedIn posts
- 1 Twitter thread
- 3 email newsletter snippets
- Maintain brand voice throughout
⏱️ 20 min
Video Recording Script (47 minutes)
Introduction + The Problem
Show 5 terrible AI outputs from vague prompts. Establish why precision matters for business.
PRECISION Framework Deep Dive
Each letter explained with 2 examples. Live demonstration building a prompt from scratch.
Three Business Use Cases
Support (email), Sales (lead scoring), Marketing (content). Build each prompt together.
Testing & Iteration
Run 10 test cases, fix failures, show how to debug prompts systematically.
Templates + Next Steps
Export 12 production-ready templates. Preview Day 2 (Zapier automation).
📹 Camera Instructions
Main camera: Screen recording + face cam (top right). Zoom in on code/prompts during demonstrations. Use green overlay for "bad" examples, gold for "good". End with "Resources" screen showing download link.
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