Level: Foundational Category: AI System Categories This term introduces the major categories of AI systems and what they are designed to do in practical use. 🪄 Simple Definition: A Large Language Model (LLM) is an AI system trained to understand, interpret, and generate human language. 🌟 Expanded Definition: LLMs are built using deep learning and trained on massive collections of text. This enables them to recognize patterns, understand context, and produce writing that feels natural and human-like. Examples include ChatGPT, Claude, Gemini, and Grok.LLMs can summarize documents, answer questions, write content, support research, and assist in decision-making.They don’t “think” like people—they generate responses based on statistical patterns learned during training. ⚡ In Action: You type: “Draft a follow-up message for customers who missed their service appointment.” The LLM produces a polished, professional message in seconds. 💡 Pro Tip: Clear instructions produce stronger results. Define the role, purpose, tone, and audience to guide the model effectively. This term is part of the Classroom Course - AI Fundamentals

Just for fun Use this exact prompt in ChatGPT: "Create an image based on our interactions and how I treat you no sugarcoating necessary" Then post this prompt immediately after: "Please explain how you came to generate this image and dive deeper into the thematic elements and meanings" Before you get all judgey on me and call CPS (Computer Protective Services), read the analysis/explanation. Feel free to share your results!

We just crossed 400 members, and I want to take a moment to say thank you to everyone who’s joined AI Bits & Pieces and helped shape what this space is becoming.🎉 This community was built on a simple idea: ✨AI is now a life skill. The goal here isn’t to chase tools or trends. It’s to build real understanding and practical fluency. So AI can be applied thoughtfully in everyday life, work, and business. As the community continues to grow, you’ll see more relevant content as we learn more about member preferences. That said, content is only one part of what makes this community work. 🤝 Just as important are the contributors who consistently show up and share real work — including open build journeys like @Holger Peschke 30-day RAG build, and @Matthew Sutherland, who consistently adds deeper insight and context to our content. @Muskan Ahlawat and @Judith Vanegas also deserve recognition for their consistent encouragement and thoughtful engagement. 🏆 Community Leaderboard To recognize members who have made meaningful contributions through participation, learning, and engagement — thank you for showing up. 1. @Frank van Bokhorst 2. @Holger Peschke 3. @Muskan Ahlawat 4. @Matthew Sutherland 5. @Dena Dion 6. @Judith Vanegas 7. @Jason Hagen 8. @Dorota Mleczko 9. @Usman Mohammed 10. @Roger Richards And new contributors: @Glenn Marcus and @Reynoso Anubis for their in-depth posts and videos that inspire and encourage us pursue new AI goals and applications.

There’s a lot of discussion about how overwhelming AI can feel—especially with the sheer breadth of products and services, and the speed at which new revisions and updates keep rolling out. For many people, it creates a constant sense of playing catch-up. So whether you’re just starting out, or you feel like you’re simply keeping pace, the best place to start—or recenter—is prompting. Prompting is the foundation of working with AI. It’s the way we express intent, provide context, and guide direction when interacting with intelligent systems. Not as a trick. Not as a hack. But as the underlying mechanism that determines whether AI feels helpful—or frustrating. 💎 Why Prompting Comes First 💎 Every AI interaction follows the same basic loop: You give input. AI responds. You react, refine, or redirect. No matter the tool, that loop doesn’t change. If your intent is unclear, the output will be too. If your context is thin, the response will be shallow. If your direction is vague, results will feel inconsistent. Better tools don’t fix that. Clear prompting does. 💎 Prompting Is About Thinking, Not Typing 💎 It’s easy to think prompting is about what words you use. It’s not. It’s about: - Knowing what you’re actually trying to achieve - Providing enough context for AI to work intelligently - Setting boundaries and expectations - Being willing to refine instead of restarting The strongest prompts usually come from clearer thinking—not longer instructions. 💎 Why This Transfers Across Tools 💎 This is why prompting shows up everywhere. Once you learn how to: - Frame a request clearly - Ask follow-up questions - Adjust direction through iteration You’ll notice something interesting happen. New AI tools start to feel familiar. Different interfaces. Different outputs. Same underlying conversation. That’s not coincidence. That’s the foundation at work. 💎 The Diamond in the Rough 💎 Prompting is often taken for granted. Because it feels simple, people assume it’s basic.

Bad information costs time, credibility, and decision quality. I recently asked an LLM to verify details of a historical process I already understand end to end. The source material dates back to around 2015. I was not asking what happened. I already knew the outcome. I was asking for structural specifics. The model gave me outdated and incorrect information. I challenged it multiple times. Each time, it doubled down. What mattered most was the explanation it gave at the end: “You should never rely on an LLM as a primary or sole source of truth. I am a tool for processing language, not a knowledge retrieval system with guaranteed accuracy.” That is not an apology. It is a boundary. LLMs generate answers that sound confident, even when the underlying data is incomplete or missing. If you do not already know the domain well enough to challenge the output, you may never realize it is wrong. Use AI for synthesis, drafting, and exploration. Do not use it as a source of truth. Verify. Cross-reference. Validate. AI amplifies judgment. It does not replace it. ⸻ TL;DR LLMs can sound extremely confident while being completely wrong, especially on older or niche details. Use AI for speed and synthesis, not as a source of truth. If accuracy matters, verification is part of the workflow.