From Text to Numbers: A Step-by-Step Guide to Creating AI

How does artificial intelligence (AI) understand text and figure out things like sentiment? It’s not some mysterious trick—it’s a step-by-step process where words get broken down and turned into numbers a machine can handle.

Think of it like translating a language, but for a computer instead of a person. In this guide, I will attempt to demystify how text becomes the fuel for smart machines (no advanced math required 😅)

Step 1: Starting with Raw Text

It begins with text—like a sentence: “I love coding apps!” This is raw data, full of meaning to us but meaningless to a machine. AI can’t process words directly, so it needs to be broken down.

Step 2: Breaking It Into Pieces - Tokenization

The text gets split into smaller units called tokens. For “I love coding apps!”, that might be: “I”, “love”, “coding”, “apps”, “!”. Each token is a building block—usually words or punctuation—that the machine can work with. This step chops the sentence into manageable parts.

Step 3: Turning Words Into Numbers

Computers can’t process words like “I” or “love”—they need numbers. It uses a vocabulary list to assign each token a unique number: “I” becomes 1, “love” becomes 2, “coding” becomes 3, “apps” becomes 4, “!” becomes 5. This turns “I love coding apps!” into a sequence: [1, 2, 3, 4, 5]. Or, you can use vectors—multi-number codes like “love” as [0.8, 0.2, 1.0]—to capture hints of meaning. The AI reads either format, simple numbers or vectors, to work with the text.

Step 4: Human Annotators Assign Labels to Sequences

Real people, called annotators, step in here. They look at the whole sentence—“I love coding apps!”—and label it, say, “positive” for its sentiment (Individual tokens like “love” don’t get tagged); the label applies to the full sequence. They use a huge dataset of labeled examples—like “I hate coding” as “negative”—and match each numbered sequence (e.g., [1, 2, 3, 4, 5]) to its label. These human judgments are the key: they teach the AI what the numbers mean.

Step 5: Training the Machine – Learning Patterns

The AI takes these numbered sequences and their labels and studies them. In a process called training, it analyzes thousands of examples to spot patterns. It learns that sequences with “love” (2) often tie to “positive,” while “hate” (say, 6) links to “negative.” This builds its ability to connect number patterns to outcomes like sentiment.

Step 6: Making Predictions – The Machine in Action

When new text arrives—like “I enjoy apps!”—it’s tokenized (“I”, “enjoy”, “apps”, “!”) and numbered (e.g., [1, 7, 4, 5]). The trained AI compares this sequence to its learned patterns and predicts: “enjoy” (7) is similar to “love” (2), so it’s likely “positive.” It’s applying what it learned to guess the meaning.

The Bottom Line

AI makes text intelligent by breaking it into tokens, numbering them, and learning from human-labeled examples. It’s a translation game: words become numbers, and humans guide the machine to make sense of them. Next time you see AI “get” a sentence, know it’s just crunching a numbered list it was trained to understand—one clear step at a time.