AI Isn’t Working – Its Fault or Yours?

ChatGPT disappointing you? You ask it to revolutionize your business, and it gives you answers worthy of an intern on Friday afternoon?

Welcome to the club.

I’ve complained about the machine too. As the AI addict I am, I’ve tested every AI tool on the market, hoping that one day, finally, one would understand what I really wanted.

Spoiler: the problem isn’t the AI. It’s you.

Well, it was me. And it’s fixable. Fortunately.

Complaining About the Machine, a Classic

We all start the same way: we throw a broken prompt at ChatGPT, hope for a miracle, and end up with a result as generic as a LinkedIn post about “the importance of perseverance.”

Typical example of a crappy prompt:

“Create an app to compete with Airbnb, but prettier.”

It’s like asking to get six-pack abs by eating McDonald’s every day, without ever setting foot in a gym. It doesn’t work. Period.

The problem? You gave no context, no constraints, no specific goal. The AI does what it can with what it has: nothing. Result: generic, vague, useless.

From Broken Prompts to PhD-Level Prompt Engineering

The Beginning (Copy-Paste Fog Mode)

At first, we all make the same mistake: we copy-paste vague requests, expect miracles, and wonder why it doesn’t work.

Examples:

• “Make me a marketing plan”
• “Write an article about AI”
• “Create me a website”

Result? Generic, flat answers with no added value. Normal: you gave the AI nothing to understand what you really want.

The Revelation (Prompt Engineering)

Then one day, you discover Prompt Engineering. And everything changes.

Prompt Engineering is the art of structuring your instructions so the AI understands exactly what you expect. It’s the difference between a lost intern and a pro who delivers.

The framework that changes everything:

1. Role: Who is the AI in this context?

   • Example: “You are an expert bodybuilding coach, Mr. Olympia winner.”

2. Task: What should it do precisely?

   • Example: “Build a custom biceps program for me.”

3. Context: What are the constraints, available resources?

   • Example: “Available: pull-up bar and my wife (50–70 kg).”

4. Rules and tone: What response style do you want?

   • Example: “I’m ready to invest in equipment. Use a sarcastic tone.”

5. Expected style example: Show it an output model.

   • Example: “Expected format: table with exercise, sets, reps, rest.”

6. Notes: Additional specifications.

   • Example: “I’d like to know everything I need to start tomorrow.”

With this framework, your results go from “meh” to “wow, that’s exactly what I wanted.”

When a Simple Prompt Isn’t Enough (PRD)

Why Your Ideas Stay as Drafts

You have an app idea. You throw it at ChatGPT. It gives you a generic wireframe, vague features, and you end up with… nothing concrete.

The problem: You didn’t structure your request. Without constraints, without measurable goals, AI can’t work miracles.

The Revelation (Product Requirements Document)

The solution? The PRD (Product Requirements Document). It’s the specification document product managers use to define a product before developing it.

PRD Structure:

1. Problem & audience: Who are you serving? What problem are you solving?

   • Example: “French SMBs struggling to automate their CRM.”

2. Key features: What are the 3-5 essential features?

   • Example: “Automatic CRM → Notion sync, client reminders, ROI dashboard.”

3. Constraints: Budget, GDPR, tech stack, deadlines.

   • Example: “Max budget €50k, GDPR compliant, Salesforce integration required.”

4. Success criteria: How do you measure success?

   • Example: “30% reduction in CRM data entry time within 3 months.”

Pro tip: Co-create the PRD with AI. Ask it questions, refine together, challenge assumptions. It transforms a vague draft into a concrete action plan.

Connect AI to Your Data (MCP & Deep Search)

MCP (Multi-Context Protocol)

MCP is AI’s silent revolution. It lets you connect ChatGPT (or Claude, or others) to your real tools: Google Sheets, Notion, PostgreSQL, your CRM, your databases.

Concrete example:

“Get this month’s sales from Salesforce, generate a strategic summary with key trends, and email it to me.”

Result? You save 45 minutes every Monday morning. The AI accesses your data directly, analyzes it, and delivers the essentials.

Before MCP: You manually export data, sort it, create pivot tables, cry.

With MCP: AI does everything. You validate. You move on.

Deep Search

No more generic Wikipedia answers. Deep Search (integrated in Claude or Perplexity, for example) scours the web in depth, cites sources, and gives you precise, recent answers.

Example:

“What are the agentic AI adoption trends in 2025 for French enterprises?”

Typical answer:

“According to the McKinsey 2025 report, 72% of French enterprises tested AI agents, but only 19% deployed them in production. Main barriers are governance (53%) and integration with existing systems (47%).”

Cited sources, precise numbers, 2025 context. No bullshit.

Still Not Working?

If ChatGPT still disappoints you, ask yourself these questions:

• ✓ Did you give clear instructions instead of vague teenage requests?
• ✓ Did you provide context, constraints, an expected format?
• ✓ Did you test MCP or Deep Search to access real, recent data?

If the answer is “no” to any of these questions, that’s where it breaks.

Quote: “Often, AI isn’t the problem: the user is.”

Conclusion

AI evolves fast. What blocked you yesterday works today. Tools like MCP and Deep Search are game-changers, but they won’t work miracles if you keep throwing broken prompts at them.

Structure your requests. Give context. Test advanced tools. And stop blaming AI when it’s your approach that’s broken.

Just try structuring your prompt on your next idea and tell me the result in the comments — or come complain, I love it.

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