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 The model gave me outdated and incorrect information.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.

We talk a lot about AI tools— Models. Apps. Updates. But beneath all of it, there’s one thing that quietly connects almost everything in modern AI: 💎 Prompting. Not as a trick. Not as a hack. But as the foundation—the way we communicate intent, context, and direction to AI. 💎 Prompting — often taken for granted, yet once refined, it unlocks real AI power. Over the next few posts, I’m kicking off a 5-part series called: 💎 Prompting: The Foundation for Unlocking Real AI Power We’ll explore: - Why prompting shows up everywhere, no matter the tool - Why iteration (not perfection) is the real superpower - Why some AI tools feel intuitive while others don’t - How prompting naturally enables us to expand from simple use to workflows and systems - And why there is no single “right” path when learning AI This series will reveal how such a simple act can unlock so much real capability. ➜ Part 1 starts tomorrow. ✨ AI Bits & Pieces — helping people and businesses adopt AI with confidence. Image created using “prompts” with ChatGPT.

If you’re new to the community — or you’re just starting to use (or trying to understand) ChatGPT and tools like it — this is the course for you. We’ve just added a new Classroom course: How LLMs Like ChatGPT Work This course is designed to help you understand what’s actually happening when you use ChatGPT, so you can stop guessing and start getting better results. The big idea is simple:👉 The more you understand how ChatGPT works, the better you can guide it with your prompts. What you’ll learn: - The building blocks behind ChatGPT and large language models (LLMs) - How prompts, responses, and conversation work together - Why ChatGPT answers the way it does — and why it sometimes sounds confident but gets things wrong - How this understanding helps you write clearer prompts and use AI more intentionally 📌 Quick Note: There are many LLMs similar to ChatGPT available today, like Gemini and Claude, which are covered in a separate course. In this course, we use ChatGPT illustratively to explain how LLMs work in practice. This course is: - Beginner-friendly - Plain English - Built for real-world use (not engineers) If you’ve ever wondered why ChatGPT responded the way it did — or how to steer it more effectively — this course will help.

Slash commands are Claude Code's secret weapon. But here's what most developers miss: you can create your own. Let me show you both sides of this productivity powerhouse. ⚡ 5 BUILT-IN COMMANDS YOU NEED TO KNOW /compact — Summarize your conversation to free up context tokens without losing progress. /init — Generate a CLAUDE.md file that teaches Claude your entire project structure. /memory — Edit persistent project knowledge that Claude remembers across sessions. /review — Get instant professional code review on your staged changes. /model — Switch between Sonnet (speed) and Opus (power) mid-conversation. 🛠️ 5 CUSTOM COMMANDS YOU SHOULD BUILD Create these in .claude/commands/ as markdown files: 1️⃣ /project:deploy markdown<!-- deploy.md --> Run our deployment checklist: 1. Run all tests 2. Check for console.logs 3. Verify environment variables 4. Build production bundle 5. Generate deployment summary Why: One command, zero forgotten steps. 2️⃣ /project:component markdown<!-- component.md --> Create a new React component with: - TypeScript interface - Styled-components file - Unit test file - Storybook story Follow our naming conventions in CLAUDE.md Why: Consistent scaffolding every single time. 3️⃣ /project:security markdown<!-- security.md --> Audit this code for: - SQL injection vulnerabilities - XSS risks - Exposed secrets - Insecure dependencies - Authentication gaps Provide severity ratings and fixes. Why: Your personal security consultant on demand. 4️⃣ /project:document markdown<!-- document.md --> Generate documentation for $ARGUMENTS: - JSDoc comments - README section - API endpoint description - Usage examples Match our documentation style guide. Why: Documentation that actually gets written. 5️⃣ /project:hotfix markdown<!-- hotfix.md --> Emergency fix protocol: 1. Identify root cause 2. Implement minimal fix 3. Add regression test 4. Create detailed commit message 5. List potential side effects Keep changes surgical and reversible. ``` *Why:* Stay calm under pressure with a structured approach.

Real quick question — what email platform do you mostly use? I’m shaping upcoming classroom content and want the examples to line up with how you really work, not just theory. Thanks for taking a second to vote 👍