Text In, Text Out

Everything is Just Text

When people hear about AI models like ChatGPT, they often assume they think, reason, or comprehend like humans. But at their core, LLMs don’t understand concepts—they process text, predict text, and generate text.

This realization is key to understanding both AI’s power and its limitations in business applications.

Overview: Text In, Text Out

Large Language Models (LLMs) function on a simple yet powerful principle:
👉 They take text as input and generate text as output—nothing more, nothing less.

Key Takeaways for Executives:

✅ LLMs don’t “know” things—they predict words based on training data.
✅ Everything they process is treated as text—even images, code, and data tables.
✅ The quality of their output depends entirely on the input (prompting matters).
✅ They can’t verify facts or understand context like humans do.

Instead of treating AI as a thinking machine, businesses should see it as a text-generation engine—one that’s only as good as the data and instructions it’s given.

LLMs Only See and Produce Text

No matter what task an AI performs—writing an email, summarizing a report, or generating a code snippet—all it does is process text.

For example, when you upload a spreadsheet, image, or PDF, LLMs don’t “see” the structure like a human. Instead:

A spreadsheet is converted into text-based CSV data.
An image is converted into text descriptions (via image recognition models).
A code file is treated as just another form of structured text.

💡 Key takeaway: Everything is ultimately broken down into words (tokens) before AI can process it.

Why This Matters for Businesses

Since LLMs only process text, how you format and structure your inputs directly affects their performance.

✅ 1. Prompts Are Everything

The way you phrase your request changes the output dramatically.

Example:
❌ “Summarize this report.” → Generic, unclear summary.
✅ “Summarize this report in 3 key points for a CEO, focusing on risks and opportunities.” → More precise and useful response.

✅ 2. Structured Data Works Better

LLMs struggle with raw, messy data but perform well with structured text inputs.

Example: Instead of pasting a long, unformatted document, using bullet points, numbered lists, or labeled sections improves results.

✅ 3. LLMs Can’t Verify Facts

Because AI models only predict words based on patterns, they don’t “check” if something is true or false.

Example:

If you ask, “Who was the CEO of Tesla in 1850?”
The model may generate an incorrect but plausible-sounding response.

💡 AI doesn’t “know” reality—it generates text that seems correct based on its training data.

The Business Implications of “Text In, Text Out”

Executives need to reframe how they think about AI—not as an intelligent agent, but as a text transformation tool that needs clear inputs and verification.

🔹 AI-generated content should always be fact-checked.
🔹 Better inputs (prompts, structured data) lead to better outputs.
🔹 AI isn’t a source of truth—it’s a pattern generator.

Instead of asking, “Can AI do this?”, the real question is:
👉 “How do we structure our inputs to get the best possible AI-generated output?”

Final Thoughts

LLMs don’t think, understand, or verify facts—they simply generate text based on patterns. The more businesses recognize this limitation, the better they can use AI effectively while avoiding pitfalls.