How Large Language Models Like ChatGPT Actually Work (A Practical Developer’s Guide)
Source: Dev.to
🔍 What Is an LLM, Really?
At its core, an LLM is a next‑token prediction system.
Given a sequence of tokens (words or word pieces), the model predicts the most likely next token — repeatedly — until it produces an answer.
- No reasoning engine.
- No memory.
- No understanding in the human sense.
Just probability distributions learned from massive data.
🧠 Pre‑Training: Learning Language Patterns
LLMs are pre‑trained on huge text corpora (web pages, books, documentation, and code).
The training objective is simple: predict the next token as accurately as possible.
From this, the model learns:
- Grammar and syntax
- Semantic relationships
- Common facts and patterns
- How code, math, and natural language are structured
This makes LLMs excellent pattern recognizers, not truth engines.
🏗 Base Models vs Instruct Models
Base model
- Can complete text
- Doesn’t reliably follow instructions
- Has no notion of helpfulness
Instruct model
- Fine‑tuned on instruction–response datasets
- Learns to answer questions and follow tasks
- Behaves more like an assistant
This is why ChatGPT feels very different from raw GPT models.
🎯 Alignment & RLHF
To make models useful and safe, alignment techniques like Reinforcement Learning from Human Feedback (RLHF) are applied.
Process (simplified)
- Humans rank model outputs.
- A reward model learns preferences.
- The main model is optimized toward higher‑quality answers.
This improves clarity, tone, and safety — but also introduces trade‑offs like over‑cautious responses.
🧩 Prompts, Context & Memory Illusions
Every interaction includes:
- System instructions
- User prompt
- A limited context window
The model:
- Has no long‑term memory
- Only “remembers” what fits in the context window
- Generates responses token by token
Once the context is gone, so is the memory.
⚠️ Why LLMs Hallucinate
Hallucinations happen because:
- The model optimizes for plausible text, not truth
- Missing or ambiguous data is filled with likely patterns
- There’s no built‑in fact verification
This is why grounding techniques matter in production systems.
🛠 How Production Systems Improve Accuracy
Real‑world AI systems often use:
- RAG (Retrieval‑Augmented Generation)
- Tool calling (search, calculators, code execution)
- Validation layers and post‑processing
LLMs work best as components in a system, not standalone solutions.
🔚 Final Thoughts
Understanding how LLMs actually work helps you:
- Write better prompts
- Design safer systems
- Set realistic expectations
- Avoid over‑trusting model outputs
If you’re building with AI or transitioning into AI engineering, these fundamentals are essential.