Hello ,

Imagine you're chatting with your best friend.

Mid-conversation, you probably find yourself unconsciously predicting what they'll say next..

Your brain is naturally anticipating future words based on context and experience.

Believe it or not, this is exactly how Large Language Models (LLMs) operate, too. They interpret patterns in language data and predict logical words or sentences that could follow.

A Brief History (1970-2025)

Language models have come a long way.

Early attempts at getting computers to understand language were based on simple rules and pattern matching.

Everything shifted in 2017 with Google’s landmark “Transformer” paper.

Unlike earlier methods that read text word by word, Transformers can take in an entire sentence at once, mapping how each word connects to every other word.

It’s closer to how humans grasp meaning, by seeing the full structure of a sentence rather than marching through it one word at a time.

Demystifying Transformers: A Friendly Breakdown

Let’s simplify step-by-step how LLMs (using Transformers) really understand language:

Step 1: Breaking Text into “Tokens”

First, the LLM breaks sentences down into meaningful pieces called tokens—usually words, punctuation, or even shorter fragments.

Example sentence: "Coach, give me motivation."

This might become tokens: ["Coach", ",", "give", "me", "motivation", "."]

Step 2: Turning Words into Numbers

Next, each token is converted into numerical representations, called vectors.

A vector is just an array of numbers that captures the meaning of a token within a mathematical space.

Think of it as giving coordinates to words on an enormous “language map”.

• Similar words ("cat" and "kitten") end up close together on this map.

• Distantly related or totally different words appear far apart.

These vectors capture fine details: grammar, emotion, intensity, and the relationships between ideas.

But why turn words into numbers at all?

Because once words are mapped into numbers, surprising patterns emerge.

For example, the vector difference between “king” and “queen” is similar to the difference between “man” and “woman.” Relationships we sense intuitively show up as clear, mathematical shifts.

To capture these layers of meaning, models represent each token with thousands of numerical values. They also track each word’s position in a sentence, almost like assigning a street address on the map.

After all, “I love cats” and “Cats love I” use the same words but mean totally different things.

Step 3: Self-Attention — Letting Words 'Talk' to Each Other

Here comes the magic

Using a method called self-attention, transformers allow each token to scan its surroundings and figure out which nearby words matter most.

Consider these two sentences:

"She withdrew cash from the bank."

"They picnicked near the river bank."

Clearly, the meaning of bank depends on surrounding words like "cash" or "river."

LLMs measure how relevant words are to each other using vector math (called a "dot product," multiplying and summing related numbers). For example, simplified numerically:

The transformer quickly realizes "bank" relates more closely to either "money" or "river" based on surrounding context, instantly adjusting meanings accordingly.

During training (on massive datasets), attention layers continually fine-tune these numerical patterns, gradually becoming superb at decoding subtle language relationships.

Step 4: Filtering Noise — Refining Meaning

Even after self-attention, language data remains cluttered. Transformers have built-in "filter" layers ("feed-forward networks").

These are quick-fire diagnostic questions for every word vector:

• “Is this a noun? A verb? Is it about a person?”

• “Does it actually help predict what comes next?”

These checks sharpen the information, stripping away irrelevant clutter.

As vectors pass through each layer, they evolve. What starts as a simple idea—say, “king”—gathers context and depth.

By the later layers, it might represent “a king ruling a medieval kingdom,” with layers of meaning shaped by the surrounding words.

Step 5: Predicting the Next Word

After passing through layers of attention and filtering, the model is ready to predict.

It uses the final vector in the sequence to summarize everything it has processed so far.

Example Prediction: "The sky is..."

These probabilities are calculated using a technique called "softmax," ensuring they neatly total 100%. The model then picks the word with the highest probability, naturally continuing the sentence..

…“blue” in this case.

What About Creativity & Accuracy?

There's a special "creativity dial" called temperature. This parameter adjusts creativity:

• Low temperature (close to 0): Creates cautious, predictable answers (ideal for factual accuracy/summaries).

• Higher temperatures (closer to 1): Produces vivid, inventive, or unusual replies. Perfect for creative writing, brainstorming unusual ideas, or storytelling.

▶️ Try it out: Using ChatGPT on the playground, write a prompt twice—once with temperature at 0.3, once at 1.2. Notice the difference!

Crypto Connections: Why Does All This Matter?

LLMs are opening revolutionary possibilities in the crypto world:

• Decentralized LLMs and Knowledge Economies: Platforms like Bittensor reward users with crypto tokens for providing AI computing power and valuable data.

• Natural Language Interfaces for Blockchain Apps: Imagine DAOs or DeFi dashboards where users navigate everything through simple, intuitive conversations.

• AI-Driven Audits: Smart tools that constantly scan and audit smart contracts, instantly spotting vulnerabilities and saving millions.

• Data DAOs: Communities built around specific datasets, where contributors are rewarded whenever their data improves AI outputs.

Crypto entrepreneurs building at this intersection believe LLMs could make blockchain apps intuitive, secure, and scalable for millions worldwide.

The Big Picture

The next wave of innovation is already building, and the tools are within reach.

By understanding LLMs and blockchain today, you’re stepping into a position to lead it. Every major leap in history began with a few people willing to learn early. Now, that opportunity is yours.

What you create, build, and imagine with these technologies could help shape a smarter, more open future for everyone.

The story of tomorrow is being written right now. And it’s yours to help tell.

Cheers,

Teng Yan & Ravi

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