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What You See Is Not What You Get (WYSIWYG) in AI

 


Why WYSIWYG Is Not True for AI

If you’ve been around technology for a while, you’ve probably heard the term WYSIWYG – What You See Is What You Get. It came from visual editors where the screen showed you exactly what would be printed or published.

With AI, especially tools like ChatGPT, that idea is dangerously misleading.
With AI, what you see is not what you get.

The FOMO Trap: “ChatGPT Will Do Everything for Me”

We’re in a moment of massive FOMO around AI.
You see headlines, demos, Twitter/X threads, LinkedIn posts – and it’s easy to think:

“If I just use ChatGPT (or any popular chatbot), it’ll handle everything I ever wanted to do.”

That is wrong.

For most people and organizations, blindly relying on a general-purpose chatbot is like hiring a very confident intern who:

  • Has read a lot,
  • Talks smoothly,
  • But is not accountable for mistakes
    …and you rarely know when they’re wrong.

If you’re using a free or cheap LLM plan and treating its answers as “truth,” you’re doing it wrong. In fact:

You often have to do double the work to verify the answers if you care about accuracy.

You copy-paste, cross-check, Google, ask colleagues, test, and review – all because the model:

  • Doesn’t know your business,
  • Doesn’t know your data,
  • And is optimized for sounding good, not being right.

The Real Value of AI Is Not in Chatty “Knows-Everything” Bots

ChatGPT-style bots are impressive. They:

  • Write text,
  • Summarize documents,
  • Draft emails and code,
  • Answer questions on almost any topic.

But that doesn’t mean they’re the core value of AI for your organization.

The real value of AI is not:

  • A generic chatbot that pretends to know everything.
  • “Ask me anything” magic oracles.
  • Demos that look great but don’t plug into your real workflows.

The real value of AI is in its ability to:

  1. Automate repetitive work
  2. Analyze large volumes of data
  3. Give predictive, data-backed answers
  4. Do all of this on your specific data, safely and reliably

And that brings us to a core principle: RAG.

RAG: Using AI Only on Your Data

A lot of the practical power of AI today comes from a pattern called RAG – Retrieval-Augmented Generation.

In plain language, RAG means:

  • You don’t ask the AI to “know everything.”
  • You do give it access to specific, trusted data sources – your:
    • Documents
    • Tables
    • Databases
    • Knowledge bases
    • Policy docs
    • Product catalogs
    • Customer histories

Then you tell the model:

“Answer only based on this data.”

So instead of:

“Hey chatbot, what should my marketing strategy be?”

You’re doing:

“Based on these 500 customer interviews, this last year of sales data, and this product roadmap, identify patterns and propose a marketing strategy.”

The AI isn’t hallucinating from the internet; it’s reasoning over your reality.

That’s where AI becomes:

  • More reliable
  • More accountable
  • And much more valuable

Why Free LLMs Won’t Give You Real Efficiency

Let’s be blunt:

Free and generic LLMs are often marketing, not solutions.

They’re fantastic for:

  • Demos
  • Learning
  • Light brainstorming
  • Personal experiments

But they are not, by themselves:

  • A transformation strategy
  • A productivity system
  • A trustworthy decision engine for your business

If you’re serious about efficiency and impact, you need to:

  • Stop thinking, “We’ll just use a free chatbot and become AI-powered.”
  • Start thinking, “How do we plug AI into our data and workflows?”

Treat free tools as:

  • A starting point, not a strategy.
  • A way to get familiar, not a way to run your business.

Narrow the Scope: Focus AI on What You Do Best

The most effective uses of AI today are narrow, not broad.

Instead of:

  • “We want AI for everything,”

Think:

  • “We want AI to make this one core thing we do faster, smarter, and more reliable.”

For example:

  • If you’re in healthcare, focus AI on analyzing patient histories, triage notes, and outcomes to assist clinicians – not on writing generic blog posts.
  • If you’re in finance, use AI to scan transactions, detect anomalies, or forecast risk – not as a generic “business advice” chatbot.
  • If you’re in SaaS, let AI:
    • Read user tickets,
    • Cross-reference product docs,
    • Propose resolutions and next-best actions.

In each case, AI is:

  • Narrowly scoped
  • Deeply connected to your data
  • Optimized for your domain

This is where AI becomes a force multiplier, not a toy.

Analyzing & Predicting > Generating Pretty Text

LLMs are amazing at generating:

  • Text
  • Code
  • Images (with the right tools)

But in terms of business value, the most underappreciated superpowers are:

  1. Analyzing

    • Comparing hundreds of documents
    • Spotting patterns in feedback
    • Classifying, tagging, clustering data
    • Extracting key fields from messy text

  2. Predicting

    • Forecasting trends based on your historical data
    • Identifying which customers are likely to churn
    • Estimating demand, risk, or opportunity
    • Surfacing which leads are worth your sales team’s time

When you combine:

  • Your data
  • Your domain
  • A focused use case

…AI becomes far more accurate and useful at analyzing and predicting than it is at just generating another paragraph of text.

So How Should You Think About AI?

If you remember nothing else, remember this:

  1. WYSIWYG does not apply to AI.
    Just because the output looks polished doesn’t mean it’s correct.

  2. General chatbots are not your endgame.
    They’re a good interface, not a full solution.

  3. You must do extra work to verify generic AI outputs, especially on free or cheap plans.
    If you care about correctness, review is not optional.

  4. The real value of AI is in your data and your workflows.
    RAG and data-specific setups beat “ask me anything” bots for serious use.

  5. Narrow beats broad.
    Focus AI on what your organization already does best—and make that better, faster, and smarter.

  6. Use AI where it analyzes and predicts, not just where it writes.
    That’s where you’ll see sustainable, compounding value.

If you’re using AI today, the question isn’t

“Which chatbot should I be using?”

It’s:

“Where in my organization do we have repetitive work or untapped data—and how do we point AI exactly there?”

That’s when what you get from AI starts to truly matter—far more than what you see in a flashy demo.

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