Blog

Innovation and new ideas lightbulb concept

The Smart Way to Write Better Prompts: Why Prompt Chaining WorksWhen people try using tools like ChatGPT or Claude for work, they often expect magic. You type in a question, and you get a perfect answer.Except… you usually don’t.Instead, the result is vague, too long, too short, or simply misses the point. So you rewrite your prompt, tweak a few words, maybe try again later. This cycle frustrates even the most curious users and often pushes teams away from using these tools consistently.But there’s a better way—and it’s simpler than you might think.Rather than throwing a big, complex request at the tool and hoping for the best, there’s a technique called prompt chaining. It means breaking your big task into smaller, more manageable prompts—then stacking them like building blocks. It’s structured, easy to learn, and works far better for most business use cases.A better way to talk to LLM's:Think of it like working with a junior team member. If you brief them with a wall of text and expect a full report in 10 minutes, it won’t end well. But if you guide them step-by-step—first get the facts, then group the ideas, then write the summary—you’ll usually get a better result. AI works the same way.Prompt chaining gives you more control. You’re not just writing one prompt and hoping. You’re guiding the process. You’re giving the model the steps it needs to think clearly, solve a problem, or complete a task with focus.A real-world example: Writing a business caseLet’s say you’re trying to draft a short business case for a new service line.Instead of asking: "Write me a business case for launching AI training for hospitality teams in regional hotels."You break it into steps like:&nbsp;“What are the three most common skill gaps in regional hotel teams when it comes to AI?”“Summarise the business impact of these gaps in plain language.”“Now write three goals a business case should focus on to address those gaps.”“Generate an executive summary for a business case that includes these three goals.”<blockquote>This takes less than five minutes. But the quality of output? Night and day compared to a one-line prompt.</blockquote>Why this matters for your businessPrompt chaining isn’t just about better results—it’s about building usable workflows that anyone in your team can follow. It reduces guesswork. It helps people think in steps. It encourages repeatable processes you can refine and improve.And when paired with no-code platforms or low-code tools, it gets even more useful.You can automate the whole process. Imagine setting up a simple button where your team selects a task—like summarising meeting notes or drafting job descriptions—and behind the scenes, prompt chains do the heavy lifting. No coding. No AI expertise required. Just a smart system that follows a path.Start small. Build fast.You don’t need a huge IT project to get going. You just need to think in steps.We help hospitality businesses, service firms, and growing teams implement AI tools using low-code and no-code platforms. That means we take care of the setup, map out smart workflows (like prompt chaining), and make sure it all runs behind a simple, easy interface.It’s fast, practical, and accessible—even if you’ve never used AI properly before.Quickly integrate AI into your hospitality business using low-code platforms. Our approach simplifies the process, making it accessible and efficient for establishments of all sizes—ensuring swift implementation without technical complexity.

Want Better Results from AI? Try This Simple Prompt Strategy

Gemini word, zodiac sign, on a push button

When tech companies announce new updates, it’s easy to switch off. More jargon. More buzz. But sometimes, an update signals something more important—a shift in what’s now possible.That’s what Google DeepMind’s latest Gemini AI update does. And it’s worth your attention.Whether you’re running a small venue, managing a growing team, or looking for smarter ways to get things done, this is the kind of change that might quietly move your business forward. Not because it’s flashy. But because it’s useful.A Smarter AI That Understands More Than Text The latest Gemini update isn’t just a performance boost. What makes it different is how it works across formats.This AI doesn’t just process written words—it can interpret video, audio, and images too. That’s what the tech world calls multimodal. But here’s what that means in plain terms:<ul><li>You could feed it a training video and ask for a summary in dot points.</li><li>You might upload a photo of a broken machine and get a suggested fix.</li><li>You could drag in a PDF, ask a question, and get a straight answer—instead of having to scroll for it.</li></ul>It’s like having a colleague who can watch, read, listen, and answer back. One that doesn’t sleep, and doesn’t miss detail.From Raw Power to Real-World UseGoogle has made these tools faster and more responsive. The Gemini update means fewer delays, clearer outputs, and smarter suggestions. But none of that matters unless it fits into the way you work.Here’s what that looks like in practice:<ol><li>Training and onboarding: Imagine building a simple assistant that can answer staff questions using your own SOPs and manuals—without needing to code.</li><li>Customer support: Upload support logs, add your FAQ documents, and ask Gemini to suggest responses or highlight common issues.</li><li>Marketing content: Drop in a few existing blog posts or product pages, and ask for a fresh draft that sounds like your brand.</li></ol>And all of this is possible without hiring a developer or investing in complex systems.What’s Changed for Non-Tech TeamsIn the past, using this kind of AI meant finding a software engineer, building custom models, and hoping it worked. Now, Gemini connects directly into tools that many people already use—or can learn quickly.There are no-code interfaces and drag-and-drop platforms that now let you build these assistants in hours, not months.This means your ops manager, sales lead, or team admin could set up a smart tool that removes low-value work. Think task summaries, customer replies, or inventory queries handled automatically—without a ticketing system or a dev team.Why This Matters for You The reality is that most businesses don’t need to build a new AI model from scratch.They need tools that solve everyday problems:<ul><li>Too many tasks</li><li>Not enough time</li><li>Knowledge that’s trapped in one person’s head</li><li>Systems that don’t talk to each other</li></ul>The Gemini update moves us a little closer to solving those problems in a way that’s affordable, fast, and accessible. It’s not perfect. But it’s getting easier to try, tweak, and roll out small tools that make a big difference.Get Started with AI—Without the Complexity If you’re curious about using Gemini or any other modern AI tools but don’t know where to begin, that’s exactly what our no-code and low-code solutions are built for.Quickly integrate AI into your business using low-code platforms. Our approach simplifies the process, making it accessible and efficient for establishments of all sizes, ensuring swift implementation without technical complexity.We’ll help you go from “we should be doing something with AI” to actually getting results—on your terms, at your pace.

What You Should Know About Google’s Gemini AI Update — And Why It Matters for Business

Global Internet connect Chatbot Chat with AI, Artificial Intelligence. A.I..

If you work with information-heavy content—reports, whitepapers, books, webinars, or even recorded meetings—you already know the challenge. It’s not that the material isn’t useful. It’s that there’s just too much of it, and never enough time to take it all in.That’s exactly where I found myself a few months ago. Important knowledge buried in 80-page PDFs. Key ideas trapped in two-hour videos. And no matter how well I tried to prioritise, I still couldn’t keep up.That’s when I started testing AI tools to do the reading and watching for me. Not just to get a short summary—but to actually help me understand, extract, and apply what was inside those documents without manually combing through every word.One tool in particular stood out: Gemini 1.5 Pro, part of Google’s AI Studio. I wasn’t expecting much more than a smarter version of search. But it turned out to be a proper assistant. Fast, context-aware, and surprisingly accurate—even when I uploaded massive amounts of content.Why I Stopped Reading Everything Myself There’s still a place for deep reading and focused viewing. But the reality is, in business, you don’t always have the luxury to read cover to cover or watch minute by minute. You need to extract insight quickly—especially when you're the one responsible for making decisions, advising clients, or steering strategy.AI gave me a way to stay informed without getting buried. And with tools like Gemini that handle up to a million tokens at once, I’m no longer stuck breaking documents into pieces or feeding transcripts into chatbots ten minutes at a time. I upload the full thing once, ask the questions I care about, and get straight to what matters.What surprised me most wasn’t the summarising itself—it was how easy it was to ask follow-up questions. Once I’d read the summary, I could go deeper: compare ideas across chapters, pull out quotes, or even ask for simplified explanations when something didn’t quite land.This made it useful across all kinds of content—whether I was digging into an industry report, reviewing a client onboarding video, or just trying to get to the heart of a new methodology I hadn’t seen before.It’s Not Just Time-Saving. It Changes the Way You Work. There’s a point where better tools don’t just save time—they change the way you think about what’s possible. That’s what started happening after a few weeks of using AI in this way.Instead of dreading long documents, I started collecting them. I’d throw in the full-text version and use AI to generate summaries for different purposes—quick internal briefings, training decks, even talking points for client conversations.When I sat in meetings, I didn’t feel like I was catching up. I already had the core ideas, key questions, and relevant context ready to go. All because the AI had done the grunt work ahead of time.This wasn’t about being lazy. It was about being effective.And from what I’ve seen, once you show someone this workflow—even just once—it sticks. Because it answers a pain most of us feel but rarely fix: too much information, too little time to process it properly.The No-Code Part Matters More Than You’d Think It’s worth mentioning that none of this required me to code, configure, or integrate anything. I didn’t need a data science team. I didn’t even need to download software. Everything ran through the browser, with simple file uploads or pasted text.That’s the power of no-code AI tools in 2025: they’re not built just for tech people—they’re built for real business use. For marketers. For operations teams. For HR. For anyone who deals with complex information but doesn’t want to learn Python to get a result.<ul><li>That accessibility is the real unlock. And it’s why I’m convinced more businesses—especially smaller ones—should be experimenting with these tools now. Not just to save time, but to build smarter habits and faster workflows.</li><li>You Don’t Need to Automate Everything. Just the Right Things. The idea isn’t to automate your entire workflow. It’s to identify where your time is being drained unnecessarily—and build a smarter solution around that.</li></ul>For me, it was reviewing long documents and videos. For someone else, it might be analysing customer feedback, summarising meeting notes, or translating raw research into executive-ready insights.Whatever your version is, tools like Gemini 1.5 Pro make it possible to get real value from AI without getting bogged down in complexity.If you’re curious about how this could work in your business, especially if you’re already exploring no-code or low-code tools, there’s a huge opportunity here. I’ve been working with businesses to set up lightweight, AI-powered workflows like this—using tools that are ready to go, no custom dev work required.If you're interested in implementing AI for document and video analysis in your own setup, I can help you get there—without the jargon and without a massive learning curve.You don’t need to overhaul everything. You just need to start with one problem AI can solve today.Let me know if you want to see what that looks like in your world. 3/3

How I Use AI to Cut Through Massive Docs and Long Videos Without Burning Out

If you're like most professionals, you've sat through your fair share of snooze-worthy slideshows. You know the ones—dense text, chaotic layouts, and slides that seem to multiply with every passing minute. I’ve been there. That’s why I started using ChatGPT to rethink how I build and deliver presentations. What used to take hours now takes a fraction of the time. And more importantly, the results are sharper, clearer, and a lot more engaging.Here's how I make it work—step by step.1. Start With a Blank Page and a Simple PromptThe first step is to stop staring at a blank PowerPoint and instead open ChatGPT. I usually begin with a short prompt: something like “I need to create a 10-slide presentation on [insert topic] for [insert audience].”What comes back is a rough outline, usually organised into clear sections like:<ul><li>Title and introduction</li><li>Background or context</li><li>Key insights or data</li><li>Solutions or recommendations</li><li>Next steps or calls to action </li></ul>This outline gives me a structure to build on—and saves me the headache of figuring out where to begin.&nbsp;2. Shape the Story, Don’t Just Stack SlidesOnce I’ve got the structure, I go back to ChatGPT to dig deeper into the content. This is where it really shines.Say I’m giving a talk on customer behaviour in digital spaces. I’ll ask for three bullet points per section, and maybe even a short example or metaphor to keep it grounded. It’s like having someone to bounce ideas off—someone who doesn’t get tired or tell you to “just Google it.”What I like most is that it helps me think in terms of story—not just data. Instead of dry facts, I’m guided to focus on flow: setting the scene, raising questions, showing the impact, and leading the audience towards a clear takeaway.&nbsp;3. Speed Up the Visuals Without Sacrificing QualityNow for the design side.I’m no graphic designer, and I don’t pretend to be. But I’ve found a few no-code tools that play well with ChatGPT and help me create slides that actually look decent:<ul><li>Canva or Gamma: I use these for layouts. Once I’ve got the copy from ChatGPT, I paste it into a template and it’s 80% there. The design is clean, and the formatting is taken care of.</li><li>AI image generators: Sometimes, ChatGPT will suggest a visual idea for a slide—like a metaphor or concept that could be shown with an image. Tools like DALL·E or Midjourney help me turn those ideas into something visual, quickly.</li></ul>The goal isn't to make it flashy (yet). It’s to make it clear.&nbsp;4. Tailor the Tone and Language for the RoomOne thing I’ve learnt: the best presentations don’t sound like textbooks. They sound like someone talking to you, not at you.So I use ChatGPT to tweak the tone. If I’m presenting to a senior team, I’ll ask for something concise and formal. If it’s a workshop or a webinar, I’ll ask it to sound more conversational. It’s like having an editor on hand who adapts to your audience in seconds.And yes, I still tweak things. But the heavy lifting? Done in minutes.&nbsp;5. Prep for Questions—Before You Get AskedThis part’s underrated. Before I present, I’ll ask ChatGPT: “What questions might come up during this presentation?” It usually gives me a decent list—some obvious, some that catch me off guard.Then I follow up with: “How should I respond to those questions in one or two sentences?”That way, I’m not just delivering slides—I’m ready for the room.The Payoff: More Time Thinking, Less Time FormattingSince I started using ChatGPT for presentations, here’s what’s changed:<ul><li>I spend less time stuck on structure.</li><li>I get to focus more on the message and less on the margins.</li><li>I feel more prepared going in, and less wiped out coming out. </li></ul>Whether you're a founder prepping for a pitch, or a team lead trying to make Monday’s update less of a slog—this process works. You don’t need to learn code or master ten design tools. You just need to ask the right questions, and let the AI do the first few drafts.Then you shape it, cut it, and make it yours.Simple as that.

How I Use ChatGPT to Create Presentations That Don’t Put People to Sleep

McMaster University, Hamilton, Ontario, Canada

You don’t have to look far to see the shift. AI is writing emails, screening CVs, analysing spreadsheets, and in some cases, making recommendations that used to come from managers or analysts. For many people—especially students or early-career professionals—it raises a serious question: Why spend years and money on a degree if machines might do the job anyway?It’s not a cynical question. It’s a practical one. And it’s worth asking.A Changing Landscape Automation isn’t new, but the speed of change is. What used to take decades to shift now changes in months. Large language models and generative AI aren’t just helping with tasks—they’re reshaping them. Roles in customer service, marketing, operations, and finance are being redesigned to accommodate AI tools. Some jobs will disappear. Others will evolve. New ones will be created. But it’s not always clear where or how fast these changes will happen.In that uncertainty, many people are left wondering: What’s the value of traditional education in a world being redesigned by machines?Uni Still Matters—But Not in the Same Way The real issue isn’t whether degrees are “worth it.” It’s what kind of education remains useful, and how it connects to real work. A uni degree still offers structure, depth, and a recognised credential—but employers are increasingly focused on skills, not just certificates. That includes technical fluency, adaptability, critical thinking, communication, and problem-solving—things AI still struggles to replicate in full (for now).Learning how to learn, work in teams, manage uncertainty, and build things that haven’t existed before—that’s the real advantage. And that’s the gap many traditional programmes don’t fully address. It's not about throwing away the idea of a degree. It’s about rethinking what education prepares people for.Motivation in an Automated World The rise of automation doesn’t just change what we learn—it changes why we learn. If the goal is just a stable job, the future might feel bleak. But if the goal is contribution, creativity, and a sense of direction, education still plays a vital role. The motivation shifts from qualification to capability.People are more likely to stay engaged if they can see how learning leads to impact. That might mean blending formal study with short AI courses, micro-credentials, bootcamps, or side projects. It could also mean choosing learning experiences that connect directly to work, rather than academic theory alone.Adapting Through the Right Mix There’s no single answer. Some careers will still need long-form education—medicine, law, engineering. Others will be shaped by short sprints of skills training. What matters is understanding the difference between knowledge acquisition and workplace readiness. They’re not the same thing, and in a world shaped by AI, they diverge more than ever.Students, professionals, and employers need to rethink how they prepare for change. The institutions that thrive will be those that offer flexible, applied learning that keeps up with technology.If you’re thinking about how to futureproof your team or career, our AI Bootcamp can help. We translate the noise of AI into practical strategies and tools, helping you stay relevant, efficient, and ahead of the curve.

What’s the Point of Uni if Jobs Become Automated?

Artificial Intelligence (AI) is everywhere—powering our phones, shaping social media feeds, and even making business decisions. But the language around AI can feel confusing, full of technical jargon that makes it hard to keep up.If you've ever found yourself nodding along in a conversation about machine learning, neural networks, or natural language processing—without fully understanding what those terms mean—you’re not alone. That’s why I’ve put together this AI glossary. It’s a straightforward guide to help you understand key AI concepts, so you can feel more confident when discussing or learning about artificial intelligence.Why AI Terminology Matters AI is no longer a niche topic reserved for tech experts. Whether you're a business owner, a student, or just someone curious about technology, understanding AI terminology gives you an edge. It allows you to:<ul><li>Engage in informed discussions – Whether at work, in online communities, or at networking events, knowing AI terms helps you follow (and contribute to) conversations.</li><li>Make better decisions – If you use AI-driven tools or services, understanding how they work helps you choose the right ones for your needs.</li><li>Expand your learning – AI is a constantly evolving field. A strong foundation in the basics makes it easier to grasp new advancements. </li></ul>Essential AI Terms You Should Know Here’s a breakdown of some of the most commonly used AI terms:1. Artificial Intelligence (AI) AI refers to computer systems that perform tasks typically requiring human intelligence, such as recognising speech, making decisions, and identifying patterns in data.2. Machine Learning (ML) A subset of AI where computer systems learn from data without being explicitly programmed. For example, recommendation algorithms on streaming platforms use ML to suggest content based on your viewing history.3. Deep Learning A more advanced form of machine learning that mimics the human brain using layers of artificial neural networks. Deep learning powers technologies like facial recognition and self-driving cars.4. Neural Networks Inspired by the structure of the human brain, these are algorithms designed to recognise patterns and process information in a way similar to neurons. They are a fundamental part of deep learning.5. Natural Language Processing (NLP) The branch of AI that enables computers to understand, interpret, and respond to human language. Voice assistants like Siri and Alexa rely on NLP to function.6. Large Language Models (LLMs) A type of AI trained on massive amounts of text data to understand and generate human-like language. GPT models, for example, fall under this category.7. Generative AI AI systems that create new content, including text, images, music, or code. These models, such as ChatGPT or DALL·E, can generate human-like responses or artwork based on prompts.8. Computer Vision An AI field that allows machines to interpret and analyse images and videos. It's used in facial recognition, medical imaging, and self-driving cars.9. Reinforcement Learning A machine learning technique where an AI model learns by trial and error, receiving rewards for good decisions and penalties for poor ones. This is used in robotics and game-playing AI, like AlphaGo.10. Bias in AI AI models can inherit biases from the data they are trained on, leading to unfair or inaccurate outcomes. Addressing AI bias is a key concern in making AI systems ethical and trustworthy.11. Explainability The ability to understand and interpret AI decisions. As AI models become more complex, ensuring transparency in how they make decisions is crucial for trust and accountability.12. AI Ethics The study of moral implications in AI development, focusing on fairness, privacy, and the impact of AI on society.How Understanding AI Terms Helps You Stay Ahead AI is shaping industries across the board, from healthcare and finance to education and entertainment. Whether you're looking to build AI skills for work, develop AI-powered projects, or simply stay informed, knowing these terms puts you in a better position to navigate the AI landscape.If you're ready to dive deeper into AI, our AI Masterclass is the perfect next step. You’ll get a free glossary of AI terms and a structured introduction to AI fundamentals—all designed to help you learn at a fast intro pace.Join the AI Masterclass today and start building your AI knowledge with confidence!

AI Terminology: A Glossary for Beginners

AI strategy consulting

AI seems complex...but what to do about it? Business is already doing more with less. AI feels overwhelming: too technical, too complex, too hype driven, not enough practicality, and too risky to implement without clear direction. 

So we help service businesses gain AI fluency with practical AI solutions that drive measurable results without jargon, high costs, or disruption.

AI Blog

Want Better Results from AI? Try This Simple Prompt Strategy

What You Should Know About Google’s Gemini AI Update — An...

How I Use AI to Cut Through Massive Docs and Long Videos ...

How I Use ChatGPT to Create Presentations That Don’t Put ...

What’s the Point of Uni if Jobs Become Automated?

AI Terminology: A Glossary for Beginners