5 Surprising Truths About How AI Language Models Actually Work

Summary: Five surprising truths about how AI language models really work — from tokens and sudden, scale-driven abilities to why they sometimes "hallucinate", how you can program them with plain language, and how retrieval systems make them more reliable.

Introduction

If you've used tools like ChatGPT, you know how effortlessly they can write an email, generate code, or explain a concept. That ease feels close to magic. Under the surface, however, these systems run on patterns, probabilities, and careful engineering. Understanding a few core ideas will help you use them smarter and more safely. View my LLM Concepts video below and then read on.

1. They Don’t See Words, They See Tokens

When you type a sentence, you see words and spaces. A large language model (LLM) processes a sequence of tokens. Tokens are the smallest pieces the model works with — sometimes a whole word, sometimes a subword fragment. For example, “unbelievable” might be broken into subword parts like “un”, “believ”, and “able”.

This design is powerful because it lets the model handle rare or new words by recombining familiar pieces. It also keeps the model’s vocabulary size manageable, which helps with efficiency and generalization.

2. Some Abilities Emerge Suddenly

Making a model bigger doesn't always improve it smoothly. At certain scale thresholds, models can exhibit new abilities that smaller versions lack entirely. Researchers call this phenomenon emergent abilities. For example, some models only begin to solve multi-step reasoning or reliably follow chain-of-thought prompts once they reach a very large size.

Emergent abilities mean scale can unlock fundamentally different behaviors — and that makes predicting future capabilities challenging.

3. They Aim for Plausibility, Not Truth (Hallucinations)

LLMs are extraordinary pattern predictors. Their core objective is to produce the most likely next token given the context, not to check facts. As a result, they can generate responses that sound confident and authoritative but are factually wrong — a behavior called hallucination.

Because models blend patterns from many contexts, they may invent details that fit linguistically but lack grounding. That makes them risky for high-stakes tasks where accuracy matters.

4. You Can “Program” Them Without Code

Modern LLMs support in-context learning: you change their behavior by giving instructions and examples in the prompt, without retraining the model. Two common approaches are:

• Zero-shot prompting: Give the model an instruction only — e.g., "Translate 'Good morning' to French."
• Few-shot prompting: Provide a few examples that demonstrate the desired pattern, after which the model generalizes.

Example (few-shot):

Q: Capital of Italy? A: Rome
Q: Capital of Japan? A: Tokyo
Q: Capital of Canada? A:

The model sees the pattern and completes with "Ottawa." Prompting effectively shifts programming skill from code to language design.

5. Make Them Reliable with an “Open-Book Test”

One effective way to reduce hallucinations is Retrieval-Augmented Generation (RAG). Instead of forcing the model to rely only on its static training data, RAG retrieves up-to-date, relevant documents and supplies them to the model as part of the prompt — like giving the model an open-book test.

Workflow in brief:

1. Convert documents to a searchable form (often vectors).
2. When a query arrives, retrieve the most relevant passages.
3. Include those passages in the prompt so the model can ground its answer in verifiable text.

In practice, RAG is useful for customer support, knowledge bases, and any application that needs current, verifiable answers.

Conclusion

We’ve gone from the token-level mechanics of LLMs to scale-driven emergent abilities, the persistent problem of hallucination, the practical power of prompting, and the reliability gains from retrieval systems. These concepts show that AI language models are powerful but imperfect tools — best used when you know their strengths and limits.

How should we proceed as these systems grow more capable? The short answer: combine enthusiasm for emergent capabilities with rigorous methods for grounding and verification, and design systems that treat LLMs as collaborators rather than single sources of truth.

Send me a message using the Contact Us (left pane) or message Inder P Singh (6 years' experience in AI and ML) in LinkedIn at https://www.linkedin.com/in/inderpsingh/ if you want deep-dive Artificial Intelligence and Machine Learning projects-based Training.