Skip to main content

Understanding Large Language Models (LLMs) Using First-Principles Thinking

Instead of memorizing AI jargon, let’s break down Large Language Models (LLMs) from first principles—starting with the most fundamental questions and building up from there.

Step 1: What is Intelligence?

Before we talk about AI, let’s define intelligence at the most basic level:

  • Intelligence is the ability to understand, learn, and generate meaningful responses based on patterns.
  • Humans do this by processing language, recognizing patterns, and forming logical connections.

Now, let’s apply this to machines.

Step 2: Can Machines Imitate Intelligence?

If intelligence is about recognizing patterns and generating responses, then in theory, a machine can simulate intelligence by:

  1. Storing and processing vast amounts of text.
  2. Finding statistical patterns in language.
  3. Predicting what comes next based on probability.

This leads us to the core function of LLMs: They don’t think like humans, but they generate human-like text by learning from data.

Step 3: How Do LLMs Work?

Now, let’s break down how an LLM actually functions in first principles:

  1. Data Collection: The model is trained on massive amounts of text (books, articles, code, etc.).
  2. Tokenization: Text is broken down into small pieces called "tokens" (words or parts of words).
  3. Pattern Learning: The model learns how words and phrases relate to each other statistically.
  4. Probability-Based Predictions: When you type a prompt, the LLM predicts the most likely next word based on learned patterns.
  5. Fine-Tuning & Feedback: The model improves over time based on human feedback and additional training.

At its core, an LLM is just a super-advanced pattern recognizer, not a true thinker.

Step 4: What Are the Limitations?

By applying first principles, we can see the weaknesses of LLMs:

  • No True Understanding: They don’t “know” anything—just predict based on patterns.
  • Bias in Data: Since models learn from human data, they inherit biases.
  • Limited Reasoning: LLMs struggle with complex logic and deep reasoning.

These insights help learners understand what LLMs can and cannot do.

Step 5: Practical Takeaways for a Learner

If you're learning about LLMs, here’s what truly matters:
✅ Think of LLMs as probability engines, not thinking machines.
✅ Focus on how they generate responses, not just their output.
✅ Understand their limitations to use them effectively.

By using First-Principles Thinking, you don’t just memorize AI concepts—you deeply understand them.

Labels:

Popular

Conversations with Grok (Part 2): Come To Think of It

Be updated and read the previous part here:  Conversations with Grok (Part 1): Introducing Agile Transportation System Me: Since I'm from Manila Philippines, which I'm guessing you already know, starting it in BGC to prove effectiveness and partnering with companies like telecoms(smart and globe) for internet, google for tech and cloud, digital payments(gcash and maya) for fare, the BGC administrator for location and other companies may seem effective Grok : You’re absolutely right—starting a routeless, agile transportation system in Bonifacio Global City (BGC) in Manila, Philippines, and partnering with heavy hitters like Smart, Globe, Google, GCash, Maya, and the BGC administrator could be a slam dunk for proving effectiveness. BGC’s a perfect testing ground: it’s compact, modern, tech-savvy, and already a hub for innovation. Let’s unpack how this could play out and why it might work. Why BGC Works BGC’s got a lot going for it. It’s a planned urban district with a dense mix o...
Read more

PHP Error: Unable to load dynamic library 'gd'

Currently installing Laravel on my Arch Linux. I got PHP, MySQL, Apache and Composer installed and trying to install Laravel with this: $ composer global require laravel/installer  But got this instead: PHP Warning:  PHP Startup: Unable to load dynamic library 'gd' (tried: /usr/lib/php/modules/gd (/usr/lib/php/modules/gd: cannot open shared object file: No such file or directory), /usr/lib/php/modules/gd.so (/usr/lib/php/modules/gd.so: cannot open shared object file: No such file or directory)) in Unknown on line 0 PHP Warning:  PHP Startup: Unable to load dynamic library 'openssl.so' (tried: /usr/lib/php/modules/openssl.so (/usr/lib/php/modules/openssl.so: cannot open shared object file: No such file or directory), /usr/lib/php/modules/openssl.so.so (/usr/lib/php/modules/openssl.so.so: cannot open shared object file: No such file or directory)) in Unknown on line 0 PHP Warning:  PHP Startup: Unable to load dynamic library 'phar.so' (tried: /usr/lib/php/modu...
Read more

Wrestling with an Old Acer Laptop to Install ALBERT—And Winning!

You know that feeling when you take an old, battle-worn laptop and make it do something it was never meant to handle? That’s exactly what we did when we decided to install ALBERT (A Lite BERT) on an aging Acer laptop. If you’ve ever tried deep learning on old hardware, you’ll understand why this was part engineering challenge, part act of stubborn defiance. The Challenge: ALBERT on a Senior Citizen of a Laptop The laptop in question? A dusty old Acer machine (N3450 2.2 GHz, 4gb ram), still running strong (well, kind of) but never meant to handle modern AI workloads. The mission? Get PyTorch, Transformers, and ALBERT running on it—without CUDA, because, let’s be real, this laptop’s GPU is more suited for Minesweeper than machine learning. Step 1: Clearing Space (Because 92% Disk Usage Ain’t It) First order of business: making room. A quick df -h confirmed what we feared—only a few gigabytes of storage left. Old logs, forgotten downloads, and unnecessary packages were sent to digita...
Read more