We are learning about NLP 101. In today’s newsletter, we'll break down what NLP is, how it works, and what the current state of NLP is in 2025.

In today’s edition:

• AI Deep Dive— NLP in a Nutshell [2025]

• Build Together— Here’s How I Can Help You

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[AI Deep Dive]

NLP in a Nutshell [2025]

If you want to understand how modern AI systems like GPT-5, Claude 3.5 Sonnet, and Gemini 2.5 work under the hood, you need to know NLP.

It stands for Natural Language Processing.

At its core, NLP is about bridging human language and machine language.

In 2025, this bridge is stronger than ever.

AI systems don’t just understand our words, they interpret context, emotion, and intent, then respond with reasoning and action.

Where NLP Fits in AI

Think of AI as the umbrella.

Inside it:

• Machine Learning [ML] – algorithms that learn from structured data (tables, relational databases, dataframes)

• Deep Learning [DL] – neural networks that power vision, speech, and language from unstructured data (text, images, videos, audios)

• Natural Language Processing [NLP] – The part that gives machines the ability to work with human language.

In 2025, NLP isn’t just about text anymore.

It’s multimodal text, images, audio, and even video are processed together.

And with new techniques like transformers, retrieval-augmented generation (RAG), and AI agents, we’ve moved way beyond basic text classification or translation.

The Two Branches of NLP

1) Natural Language Understanding [NLU]

This is about machines understanding meaning, context, and intent.

Context

Let’s say I am complimenting you with this, “Your T-shirt is killer”

As humans, we know, this is a positive statement.

Old NLP models saw “killer” and thought negative.

We need the machine to understand the context of it, not only focus on individual words.

Modern NLP, powered by contextual embeddings from models like GPT-5, gets it right.

Intent

Let’s say you go to the vegetable seller and say, “My mom gave me money to buy 1kg of tomatoes, otherwise she will be angry”

For the vegetable seller, will understand that you want 1kg of tomatoes, other information is irrelevant to him.

His only concern is to know your intent.

In 2025, AI agents go further, they can understand intent and act on it (like actually ordering the tomatoes).

2) Natural Language Generation [NLG]

This is about creating language, writing sentences, paragraphs, even code.

• early systems just predicted the next word

• today’s models (GPT-5, Claude 3.5) can reason, plan, and generate text at scale

• context windows have exploded: up to 1 million tokens (Claude 3.5) or 400k tokens (GPT-5), meaning entire books or codebases can be processed in one go

I shared a YouTube short of content window.

As the name suggests, you are going to generate text.

It works on generating the next word in a sentence.

When I was pursuing my master's in Data Science & AI, there was a subject, Statistical Modeling.

Generative Modeling is a branch of statistical modeling, in which you predict the next number or word based on the previous number or word, using probability.

When you see any generative AI project, it is generating text, which is nothing but the prediction of the next word in a sentence based on the probability, given the previous words.

The Big Goal of NLP in 2025

The goal has always been to make machines understand us without requiring us to learn programming. Thus, prompt engineering.

In 2025, we’re basically there.

With agentic AI workflows, you can describe a complex task in plain English and AI agents will plan it, break it into steps, and execute it.

No code needed.

Key NLP Technologies in 2025

Multimodal AI

Models don’t just read text, they also see images, hear audio, and watch video.

Check Nano Banana here by Google.

Edge Processing

NLP now runs on-device, not just in the cloud.

That means faster, more private, and real-time.

Critical for self-driving cars, healthcare devices, and wearables.

RAG

Combines LLMs with external data.

It solves hallucination problems by pulling in live, factual data.

Enterprises rely on this heavily in 2025.

Agentic AI

The leap from chatbots that reply to agents that act.

These systems don’t just respond, they execute multi-step processes, collaborate with other agents, and adapt to changes.

Applications of NLP in 2025

Traditional - but improved:

• sentiment analysis – now understands emotions and sarcasm

• summarization – can handle massive documents (millions of tokens)

• machine translation – 98% accurate, real-time across dozens of languages

• spam + fraud detection – includes deepfake and AI-generated content

• question answering – no longer static, they’re full agents that take action

What’s Next for NLP

By 2030, NLP won’t just be a feature, it will be infrastructure.

Like electricity or the internet, it will run in the background everywhere.

Expect:

• cross-platform memory, conversations that follow you from phone to laptop to car

• universal translation that eliminates language barriers entirely

• proactive AI assistants that anticipate needs before you ask

• emotional AI that responds sensitively to human feelings

• AI-human collaboration as the default in workplaces

Until next time.

Happy NLP.

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