When you ask ChatGPT to draft a report, get Claude to analyse a contract, or have Gemini generate code, it often feels like magic. The model “understands” you and delivers exactly what you need.Yet for most people the reality behind these large language models (LLMs) remains a mystery: you input text (the prompt), you get an output, and everything in between is hidden. Actually, LLM “intelligence” isn’t thinking in the human sense. It’s pattern‑learning at scale and statistical prediction. In technical terms, it’s more like a “super translator” of language: you feed it a natural‑language instruction, and it converts it into output that follows linguistic rules, logical structure and context requirements. Once you grasp how it works you’ll not only write sharper prompts but also anticipate how the model might behave (and misbehave). In this article, we’ll unpack LLMs through five key dimensions: core principle → architecture → training flow → inference process → capability boundaries. five key dimensions 1. Core Principle: It Doesn’t “Think” — It Predicts the Next Word 1.1 What’s really happening: probability over thinking LLMs predict the next token given a context (your prompt + generated words).Example: “This weekend I’m planning to go hiking, and I need to bring ______”Possible continuations: “water bottle” (35%), “sunscreen” (25%), “backpack” (20%), “snacks” (15%), “raincoat” (5%).It picks the most probable token (“water bottle”) and keeps going. “This weekend I’m planning to go hiking, and I need to bring ______”Possible continuations: “water bottle” (35%), “sunscreen” (25%), “backpack” (20%), “snacks” (15%), “raincoat” (5%).It picks the most probable token (“water bottle”) and keeps going. This makes the model output the most likely continuation — not necessarily the truth. most likely continuation truth 1.2 Underlying architecture: Transformer & attention The Transformer architecture (Google, 2017) uses self‑attention, allowing the model to focus on relevant context.E.g., “Alex picked up the book because he needed to finish his assignment.”Attention weights connect “he” → “Alex”. Multiple “heads” look from different angles (semantic, syntactic, causal). Combined, they form contextual understanding. self‑attention 1.3 The training backbone: a hidden “language knowledge graph” The model is trained on huge text corpora — books, web pages, dialogue, code — learning grammar, meaning, logic and facts by correlation.It knows “doctor ↔ hospital” and “rain ↔ umbrella” from co‑occurrence, not understanding. 2. Technical Architecture: From Input to Output in Five Modules Input processing: tokenization + embedding → converts words to numbers.
Encoding: multi‑head attention → builds context‑aware vectors.
Feature extraction: feed‑forward networks dig meaning, tone, and intent.
Decoding: autoregressively predicts next tokens, using sampling controls (temperature/top‑p).
Output processing: maps vectors back to text, formats for readability. Input processing: tokenization + embedding → converts words to numbers. Input processing: Encoding: multi‑head attention → builds context‑aware vectors. Encoding: Feature extraction: feed‑forward networks dig meaning, tone, and intent. Feature extraction: Decoding: autoregressively predicts next tokens, using sampling controls (temperature/top‑p). Decoding: Output processing: maps vectors back to text, formats for readability. Output processing: 3. Training Process: How a Blank Model Becomes an Assistant 3.1 Pre‑training: learning language Massive unlabelled data → model learns syntax, semantics, and general knowledge. 3.2 Fine‑tuning: specialising skills Smaller labelled datasets → task‑specific learning (translation, summarisation, coding). 3.3 Alignment: fitting human values & preferences Human feedback (RLHF) teaches the model to prefer polite, helpful, safe outputs. 4. Inference Walk‑through: Prompt → Output Example Prompt: Example Prompt: “Write a PRD for a smart desk lamp with features: automatic brightness, mobile app control, timed shut‑off. Include ‘Product Objective’, ‘Core Features’, ‘User Persona’, ‘Non‑functional Requirements’. ~700 words.” “Write a PRD for a smart desk lamp with features: automatic brightness, mobile app control, timed shut‑off. Include ‘Product Objective’, ‘Core Features’, ‘User Persona’, ‘Non‑functional Requirements’. ~700 words.” Input: prompt tokenised & embedded into vectors.
Encoding: detects main ideas & structure requirements.
Feature extraction: interprets task as “write PRD” with tone = professional.
Decoding: outputs tokens one by one → builds sections.
Output: formats headings/lists, adjusts to ~700 words. Input: prompt tokenised & embedded into vectors. Input: Encoding: detects main ideas & structure requirements. Encoding: Feature extraction: interprets task as “write PRD” with tone = professional. Feature extraction: Decoding: outputs tokens one by one → builds sections. Decoding: Output: formats headings/lists, adjusts to ~700 words. Output: 5. Capability Boundaries: Knowing What LLMs Can’t Do 5.1 Factual errors Models hallucinate because data may be outdated or statistical. Always verify facts. 5.2 Weak reasoning Struggles with abstract, multi‑step logic. Use “step‑by‑step reasoning” prompts. 5.3 Generic outputs Default settings favour high‑probability words → bland text. Raise temperature or add creative constraints. 5.4 Unseen concepts Can’t understand new or private data without input. Provide context explicitly. 6. LLMs vs Human Intelligence Aspect

LLM

Human



Learning

Passive pattern recognition

Active conceptual understanding



Reasoning

Probability prediction

Causal analysis



Knowledge update

Requires retraining

Continuous, self‑driven



Consciousness

None

Intentional, emotional Aspect

LLM

Human



Learning

Passive pattern recognition

Active conceptual understanding



Reasoning

Probability prediction

Causal analysis



Knowledge update

Requires retraining

Continuous, self‑driven



Consciousness

None

Intentional, emotional Aspect

LLM

Human Aspect Aspect LLM LLM Human Human Learning

Passive pattern recognition

Active conceptual understanding Learning Learning Passive pattern recognition Passive pattern recognition Active conceptual understanding Active conceptual understanding Reasoning

Probability prediction

Causal analysis Reasoning Reasoning Probability prediction Probability prediction Causal analysis Causal analysis Knowledge update

Requires retraining

Continuous, self‑driven Knowledge update Knowledge update Requires retraining Requires retraining Continuous, self‑driven Continuous, self‑driven Consciousness

None

Intentional, emotional Consciousness Consciousness None None Intentional, emotional Intentional, emotional 7. Practice Exercises Explain “next token prediction” with your own hiking example.
Compare pre‑training vs fine‑tuning for a “medical‑report LLM”.
Prompt design: lead the model through multi‑day inventory math step by step. Explain “next token prediction” with your own hiking example. Explain “next token prediction” Compare pre‑training vs fine‑tuning for a “medical‑report LLM”. Compare pre‑training vs fine‑tuning Prompt design: lead the model through multi‑day inventory math step by step. Prompt design: 8. Summary LLMs aren’t mysterious — they’re probability engines wrapped in language. Understanding their mechanics helps you write better prompts, expect realistic behaviour, and apply the right model for each task.Future models will grow, but the statistical core remains. Mastering that core makes you the real intelligence in the loop. you

Google

From Black Box to Transparent: How to Read the Inner Logic of LLMs

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