I’ve Built NLP Systems for 10 Years: Here Is How Computers Actually Read

By Rajashekar, Senior NLP Engineer Last Updated: December 2025

Back in 2015, I spent three weeks writing a complex set of rules just to help a customer support bot understand that “I lost my card” and “Where is my card?” required two totally different responses. It was a nightmare of if/else statements and regular expressions.

Fast forward to late 2025, and I can spin up a model in ten minutes that understands sarcasm, context, and intent with frightening accuracy.

That shift is due to Natural Language Processing (NLP). If you have ever wondered how ChatGPT writes code or how your spam filter knows a “Prince from Nigeria” is bad news, this is the engine under the hood.

I am skipping the textbook definitions. Instead, I’m going to walk you through how NLP works based on my decade of crashing servers, debugging tokenizers, and watching models hallucinate.

⚡ Quick Summary (For the Skimmers)

If you are in a rush, here is what you need to know:

• It’s Math, Not Magic: NLP turns words into numbers (vectors). Computers don’t “read” English; they calculate distances between number coordinates.
• Context is King: Old models read left-to-right. Modern models (Transformers) read the whole sentence at once to understand context.
• The “Pipeline” Matters: 80% of my work isn’t modeling; it’s cleaning text. Garbage data in equals garbage predictions out.
• It is not perfect: Even the best models in 2025 still struggle with ambiguity and can confidently lie (hallucinate) if you don’t implement guardrails.

What Is NLP? (The Engineer’s Definition)

Technically, Natural Language Processing is a subfield of AI that gives computers the ability to understand text and spoken words.

But practically? NLP is a translator.

Human language is messy. We use slang, we imply things without saying them, and the same word (“bank”) can mean a place to keep money or the side of a river. Computers are rigid; they like zeroes and ones. NLP is the messy bridge that forces the computer to deal with our chaotic way of speaking.

[IMAGE TIP: Take a screenshot of a raw JSON data output vs. a clean text sentence to show the “messy” data engineers actually deal with.]

How It Actually Works: The 4-Step Pipeline

When I build an application—whether it’s a sentiment analyzer for stock trading or a chatbot—I almost always follow the same four steps.

Step 1: Pre-processing (The Janitorial Work)

I cannot stress this enough: Real-world data is gross.

If I scrape data from the web, it is full of HTML tags, emojis, weird spacing, and typos. Before a model ever sees the text, I have to clean it.

• Normalization: I convert everything to lowercase. “Hello” and “hello” should be treated as the same word.
• Stop Word Removal: Words like “the,” “is,” and “and” essentially add noise. In my earlier projects, removing these improved processing speed by 30%.
• Stemming/Lemmatization: This chops words down to their root. “Running,” “ran,” and “runs” all become “run.”

My Experience: I once debugged a model for two days that was failing to categorize reviews. The culprit? I hadn’t stripped out invisible Unicode characters that were breaking the tokenizer. Always sanitize your inputs.

Step 2: Tokenization (Chopping the Vegetables)

Once the text is clean, we break it into chunks called tokens.

A token can be a word, a character, or a sub-word. In 2025, most of the high-performance models I use (like GPT-4o or Llama 3) use sub-word tokenization.

Why? Because if the model sees the word “unbelievably,” a whole-word tokenizer might mark it as “unknown” if it hasn’t seen it before. A sub-word tokenizer breaks it into un, believ, able, and y. It understands the parts of the word to guess the meaning.

[SCREENSHOT TIP: Screenshot a Python terminal running a simple tokenizer.encode("Natural Language Processing") script to show the integer output.]

Step 3: Vectorization (The “Aha!” Moment)

This is the most critical part. We have to turn those tokens into numbers.

In the old days (we’re talking 2010s), we used something called “One-Hot Encoding.” It was a massive spreadsheet where every word in the English language had a column. It was inefficient and terrible at context.

Today, we use Embeddings.

Imagine a 3D graph (or a 1,000-D graph).

• The word “King” is at coordinate [5, 3, 9].
• The word “Queen” is at coordinate [5, 4, 9].
• The word “Apple” is way over at [90, 1, 2].

Because “King” and “Queen” appear in similar contexts in the training data, their coordinates are mathematically close to each other. When I train a model, I am essentially teaching it to map these relationships.

Real-world test: If you take the vector for King, subtract Man, and add Woman, the resulting vector is mathematically closest to Queen. When I first ran this code on my laptop using word2vec years ago, it felt like magic.

Step 4: The Model (Transformers)

Since about 2017, the industry has standardized on the Transformer architecture.

Before Transformers, we used RNNs (Recurrent Neural Networks), which read text sequentially (left to right). The problem? By the time the RNN got to the end of a long paragraph, it often “forgot” the beginning.

Transformers use a mechanism called Self-Attention. When the model reads the sentence: “The animal didn’t cross the street because it was too tired.”

The attention mechanism looks at the word “it” and calculates a probability score connecting it to “animal” rather than “street.” It holds the entire context in memory simultaneously. This allows for the coherent, long-form text generation we see today.

The Tool Stack: What You Need to Start

If you are reading this and want to try it yourself, you don’t need a supercomputer. Here is the stack I currently use for 90% of my projects.

1. Python

It is the non-negotiable language of AI. Do not try to do this in Java or C++ unless you have a very specific enterprise reason.

2. The Libraries

• spaCy: This is my “workhorse.” It is industrial-strength, fast, and great for standard tasks like Named Entity Recognition (finding names/dates in text).
• Hugging Face (Transformers): This is the hub. If I need a state-of-the-art model (like BERT or RoBERTa), I pull it from here with two lines of code.
• NLTK: Good for academic learning, but I find it too slow for production apps.

3. Hardware

You can run simple NLP on a CPU. But if you want to fine-tune a model? You need a GPU.

• My Advice: Don’t buy a $2,000 graphics card yet. Use Google Colab (Free Tier). It gives you access to an NVIDIA T4 GPU for free. It is enough to learn the ropes.

[IMAGE TIP: Take a photo of your monitor showing a Google Colab notebook running a training block to prove you use the environment.]

Who Is This NOT For?

I want to be honest about the downsides. NLP is a fascinating career and hobby, but it is frustrating.

1. If you hate cleaning data: As I mentioned, 80% of the job is regex and formatting text files. If you just want to “make the robot talk,” you will be disappointed.
2. If you need 100% accuracy: NLP is probabilistic. Even the best models make mistakes. If you are building a system for medical or legal advice where an error is catastrophic, you need massive human oversight.
3. If you are on a strict budget: Using pre-trained models is cheap. Training your own models from scratch costs thousands (sometimes millions) of dollars in compute time.

My Final Verdict

Natural Language Processing has moved from “experimental research” to “essential utility” in the last few years.

When I started, we were happy if a computer could identify that a tweet was “sad.” Now, we are generating entire novels.

If you are a beginner, start small. Download Python, install spaCy, and try to build a simple program that can summarize a news article. Don’t worry about the complex math behind the vectors yet—just get a feel for how the machine “thinks.”

The gap between human language and machine code is shrinking every day, and learning how that bridge works is one of the best investments you can make in 2025.

Disclaimer: I am a software engineer and AI practitioner. While I discuss financial or technical concepts, always verify code in a safe environment before deploying to production.