Common AI Prompting Mistakes to Avoid

Stop blaming ChatGPT, Claude , or Grok for bad outputs when you're using it wrong. Here's the brutal truth: 90% of people fail at AI because they confuse prompt engineering with context engineering. They're different skills. And mixing them up kills your results. The confusion is real: People write perfect prompts but get terrible outputs. Then blame the AI. Plot twist: Your prompt was fine. Your context was garbage. Here's the breakdown: PROMPT ENGINEERING = The Ask CONTEXT ENGINEERING = The Setup Simple example: ❌ Bad Context + Good Prompt: "Write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." AI gives generic corporate fluff because it has zero context about your business. ✅ Good Context + Good Prompt: "You're our sales director. We're a SaaS company selling project management tools. Our Q4 goal is 15% growth. Our main competitors are Monday.com and Asana. Our ideal clients are 50-500 employee companies struggling with team coordination. Previous successful emails mentioned time-saving benefits and included customer success metrics. Now write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." Same prompt. Different universe of output quality. Why people get this wrong: They treat AI like Google search. Fire off questions. Expect magic. But AI isn't a search engine. It's a conversation partner that needs background. The pattern:  • Set context ONCE at conversation start • Engineer prompts for each specific task  • Build on previous context throughout the chat Context Engineering mistakes:  • Starting fresh every conversation  • No industry/role background provided  • Missing company/project details • Zero examples of desired output Prompt Engineering mistakes:  • Vague requests: "Make this better" • No format specifications  • Missing success criteria • No tone/style guidance The game-changer: Master both. Context sets the stage. Prompts direct the performance. Quick test: If you're explaining your business/situation in every single prompt, you're doing context engineering wrong. If your outputs feel generic despite detailed requests, you're doing prompt engineering wrong. Bottom line: Stop blaming the AI. Start mastering the inputs. Great context + great prompts = consistently great outputs. The AI was never the problem. Your approach was. #AI #PromptEngineering #ContextEngineering #ChatGPT #Claude #Productivity #AIStrategy Which one have you been missing? Context or prompts? Share your biggest AI struggle below.

You write bad prompts (and blame ChatGPT). Here are 7 common prompting mistakes: 1. Missing context (“Review this” with no background) 2. Providing vague instructions (“Write about startups”) 3. Using AI like Google (asking questions vs giving instructions) 4. Asking for several things at once (15 tasks in one prompt) 5. Not asking for changes (sending a prompt and expecting perfection) 6. Not defining tone or format (leading to random responses) 7. Not sharing examples (no reference for ideal output) The best prompts follow a simple framework: ☑ Clearly define the task + the role AI should act as ☑ Provide one or more strong examples of ideal output ☑ Give feedback after initial response and continue refining In other words: 1. Assign a role + share clear instructions 2. Show examples of what “good” looks like 3. Iterate like a real creative partner This is "AI + Human". Not "AI instead of Human". 📌 Get Advanced ChatGPT Guide (free): https://bit.ly/3StIB3z 👉 Follow me Andrew Bolis for more and 🔄 Repost this to help others use AI

I spent 1,000+ hours figuring out Prompt Engineering Here's everything I learned distilled into 12 rules you can use right now: 1️⃣ Understand the tool A prompt is how you talk to a language model. Better input = better output 2️⃣ Choose your model wisely GPT-4, Claude, Gemini—each has strengths. Know your tools 3️⃣ Use the right technique ↳ Zero-shot: ask directly ↳ Few-shot: show examples ↳ Chain-of-thought: guide the model step by step 4️⃣ Control the vibe Tune temperature, top-p and max tokens to shape output 5️⃣ Be specific Vagueness kills good output. Say exactly what you want 6️⃣ Context is king Add details, background, goals, constraints—treat it like briefing a world-class assistant 7️⃣ Iterate like crazy Great prompts aren’t written once—they’re rewritten 8️⃣ Give examples Format, tone, structure—show what you want 9️⃣ Think in turns Build multi-step conversations. Follow up, refine, go deeper 🔟 Avoid traps ↳ Too vague → garbage ↳ Too long → confusion ↳ Too complex → derailment ↳ Biased input → biased output 1️⃣1️⃣ One size fits none Customize prompts by task—writing, coding, summarizing, support, etc. 1️⃣2️⃣ Structure is Your Friend: Use headings, bullets, XML tags, or delimiters (like ```) to guide the LLM's focus Mastering these isn't optional—it's how you unlock the *real* power of AI. It's leverage. Which rule do you see people ignore the MOST? 👇 Repost this to help someone level up their prompting game! ♻️

Whether you're using Replit Agent, Assistant, or other AI tools, clear communication is key. Effective prompting isn't magic; it's about structure, clarity, and iteration. Here are 10 principles to guide your AI interactions: 🔹 Checkpoint: Build iteratively. Break down large tasks into smaller, testable steps and save progress often. 🔹 Debug: Provide detailed context for errors – error messages, code snippets, and what you've tried. 🔹 Discover: Ask the AI for suggestions on tools, libraries, or approaches. Leverage its knowledge base. 🔹 Experiment: Treat prompting as iterative. Refine your requests based on the AI's responses. 🔹 Instruct: State clear, positive goals. Tell the AI what to do, not just what to avoid. 🔹 Select: Provide focused context. Use file mentions or specific snippets; avoid overwhelming the AI. 🔹 Show: Reduce ambiguity with concrete examples – code samples, desired outputs, data formats, or mockups. 🔹 Simplify: Use clear, direct language. Break down complexity and avoid jargon. 🔹 Specify: Define exact requirements – expected outputs, constraints, data formats, edge cases. 🔹 Test: Plan your structure and features before prompting. Outline requirements like a PM/engineer. By applying these principles, you can significantly improve your collaboration with AI, leading to faster development cycles and better outcomes.

Many professionals are leaning into AI but not all are getting the value they expect. The reason? Prompt writing mistakes. If you’ve ever asked ChatGPT a question and felt underwhelmed by the answer, you’re not alone. One of the biggest mistakes is failing to provide enough context. Vague prompts like “What’s the best strategy?” won’t get you far. Be specific. Try, “What’s the best strategy for reaching decision-makers in mid-market SaaS companies?” Another misstep? Treating it like a one-shot tool. Think of prompt writing as a conversation, not a command. If the first response doesn’t hit the mark, ask a follow-up, clarify, or adjust your ask. Want richer output? Use prohibitions. For instance, “Share 3 lead gen tactics that don’t involve cold calling” sets clearer expectations. Also, keep it simple. Overcomplicating your language can confuse the AI. The best prompts are clear, direct, and aligned with your intent. These tweaks can completely change your results. Are there tips you’ve found helpful when using AI tools like ChatGPT? If so, please drop them in the comments below. #promptwriting #ai #aiforsales