As the AI landscape evolves, now is the time to become a transformation partner for businesses adopting AI. In my latest video, I break down exactly how to run a $60k AI Audit for companies like we do at my agency Morningside. You’ll learn how to identify opportunities and package your findings into reports that can even land development deals, making you indispensable as their new AI Transformation Partner. Perfect for: ✅ Consultants who want to add AI strategy to their services ✅ Freelancers looking to charge more for advisory work ✅ Anyone wanting to get a foot in the door helping businesses implement AI Check out the video and drop your questions below! 🔗 https://youtu.be/noivN2hIXLY 🚀 Get My FREE AI Audit Guide: https://liam-ottley.kit.com/02272ce8f2

Since I’ve been bombarded with questions about GoHighLevel, "what it actually is" and "whether it’s relevant to my business", I figured it’s time to break it down. Here’s everything you need to know. WHAT IS GOHIGHLEVEL? GoHighLevel is an all-in-one platform built for agencies. Instead of using 6 different tools, you get one system that does it all. Funnels. CRM. Emails. SMS. Calendars. Payments. Automations. All under one login. You can capture leads, talk to them, book them, sell to them, and track everything, without switching tabs. WHO IS IT FOR? Agencies. Freelancers. Coaches. Consultants. Creators. Anyone running a service business that needs to: - Get leads - Follow up fast - Close deals - Deliver results WHAT CAN IT DO? CAPTURE LEADS - Build funnels and websites - Add forms and surveys - Run ads that send traffic to your landing pages NURTURE LEADS - Send emails, texts, calls, or DMs automatically - Use workflows to trigger smart follow-ups - Book appointments with built-in calendars CLOSE DEALS - Manage your pipeline with drag-and-drop boards - Track every stage of your sales process - Collect payments with Stripe integration DELIVER VALUE - Create client dashboards - Run memberships or courses - Automate onboarding and retention workflows WHY AGENCIES ARE SWITCHING Before: ClickFunnels for funnels Calendly for bookings Mailchimp for emails ActiveCampaign for automations Zapier to glue it all together Google Sheets to track leads Now: One tool. One login. Lower cost. Less mess. GoHighLevel replaces 5–10 tools with one system built specifically for agency workflows. KEY FEATURES FUNNEL BUILDER Drag-and-drop editor for pages that convert. No code needed. Includes templates. FORM + SURVEY BUILDER Create branded lead capture forms and surveys. Embed anywhere. CRM Track every lead. Add notes, tags, custom fields. Filter your contacts. See full history. CALENDAR Let leads book meetings. Sends auto-reminders. Syncs with Google Calendar.

Prerequisites: LLMs, Prompts, and What You Really Need to Know How LLMs Are Trained (And Why It Matters) You’re not training your own language model, but it helps to know what’s happening under the hood. There are three main stages to how LLMs are trained: 1. Pre-Training This is the brute-force phase. The model reads everything, books, articles, websites. Trillions of words. It learns by guessing the next word over and over. No human correction. Just pattern recognition at massive scale. Result? A model that understands grammar, tone, sentence structure, and general knowledge. But it’s still raw. It doesn’t follow instructions or know what you want. 2. Fine-Tuning Now the model gets focused training. It’s fed curated examples: prompt → ideal response. This stage teaches it how to follow instructions properly. It’s where “write a poem” turns into something actually worth reading. Result? More helpful, less robotic. Starts to sound like it gets you. 3. Human Feedback (RLHF) Real people score multiple responses from the model. The model learns what humans prefer and starts optimizing for those answers. This makes it more aligned. More useful. Less weird. What’s Actually Happening When an LLM Writes Text Here’s the process in plain English: 1. Your message is split into chunks (tokens) 2. Those chunks get turned into numbers (vectors) 3. The model looks at the whole input, figures out context 4. It predicts the next token (aka the next word or word-piece) 5. It adds that to the response 6. Repeats until it decides to stop Underneath all this is a thing called a transformer — the model architecture that makes this possible. You don’t need to know the math. Just know this is what lets it "read" your input properly and respond with context. Challenges of Training LLMs This process is not cheap or clean: * Requires thousands of GPUs running for weeks * Clean, balanced data is hard to find * Hard to make it behave safely or ethically * Tough to evaluate what “good” even means

Prerequisite: TOKENIZATION & TEXT PROCESSING (How AI breaks down text to understand and generate language) WHAT IS TOKENIZATION → Tokenization is the process of splitting text into smaller units called tokens → Tokens can be words, parts of words (subwords), individual characters, or even punctuation → Every AI model starts by analyzing tokens, this is how it reads and processes text → Clean tokenization = better accuracy, faster processing, and fewer mistakes TOKENIZATION TECHNIQUES Different ways to split text, depending on the language and use case WORD TOKENIZATION → Breaks text based on spaces → Example: "AI is transforming industries" → ["AI", "is", "transforming", "industries"] SUBWORD TOKENIZATION → Splits words into smaller meaningful parts like prefixes and suffixes → Handles rare or made-up words better → Example: "unhappiness" → ["un", "happiness"] CHARACTER TOKENIZATION → Treats each character (including spaces and punctuation) as a token → Useful for very detailed processing or languages without clear word boundaries → Example: "AI"→ ["A", "I"] BYTE-PAIR ENCODING (BPE) → Merges the most frequent pairs of bytes in a word → Balances efficiency with vocabulary coverage → Example: "lower" → ["low", "er"] SENTENCE TOKENIZATION → Splits large chunks of text into sentences → Helps AI preserve structure and meaning → Example: "AI is evolving. It impacts many sectors." → ["AI is evolving.", "It impacts many sectors."] TOKENIZATION EXAMPLE How the sentence “n8n is such a powerful tool!!” gets split → Word Tokenization: ["n8n", "is", "such", "a", "powerful", "tool!!"] → Subword Tokenization (BPE): ["n", "8", "n", "is", "such", "a", "powerful", "tool", "!!"] → Character Tokenization: Every letter, space, and punctuation is separate → OpenAI Tokenization: ["n8n", " is", " such", " a", " powerful", " tool", "!!"] IMPACT ON EMBEDDINGS Why token choice affects how text becomes numbers GRANULARITY → Word-level: Fewer tokens, but struggles with rare words

Prerequisites: What AI Is and How It Works What the hell is AI, really? Artificial Intelligence is when computers start acting a little too smart for comfort. Not sentient, not emotional, just good at doing tasks humans are usually paid to think through. Stuff like: * Understanding language * Spotting patterns * Solving problems * Making decisions The goal isn’t to build robot brains. It’s to replicate pieces of human thinking so machines can handle more of the boring (or complex) work. Types of AI Not all AI is the same. You’ve probably already used three out of four without realizing it. Here's the breakdown: 1. Narrow AI Also called Weak AI. It does one job well. That’s it. Think: * Siri answering your weather questions * Netflix knowing your next binge * Your inbox pushing spam out of sight This is the AI that’s already baked into your daily life. Quiet but everywhere. 2. Predictive AI It looks at mountains of data and tries to guess what’s coming. Forecasts sales. Flags a customer likely to leave. Spots a machine about to break down. It’s not magic. Just math, statistics, and pattern matching at scale. Useful for planning and decision support. 3. Generative AI This is the stuff you’ve seen blow up: ChatGPT, Midjourney, DALL·E. It creates. Words. Images. Music. Code. All learned from massive piles of internet data. It doesn’t “think” like a human. It just gets scary good at copying the shape of human creativity. Right now, it’s the flashiest, fastest-evolving part of AI. 4. General AI Still sci-fi. Still theoretical. This would be AI that can do anything a person can. Reason. Feel. Adapt. You’ve seen it in movies. We’re not there yet. How does AI actually work? Short version: It learns from data. Longer version: 1. Data Collection – Feed it massive sets of info. 2. Pattern Recognition – It looks for connections. 3. Model Building – It builds a system based on those patterns. 4. Prediction/Action – It uses the system to guess, decide, or do something when new input comes in.