AI is not magic – why developers should understand how it actually works

Most developers now have some experience with ChatGPT, GitHub Copilot, or Claude. We use them to write code, brainstorm ideas, summarize texts, and solve problems, and they work impressively well. So well, in fact, that it almost feels like magic.

But the truth is: there’s no magic here – just a lot of math, machine learning, and smart engineering.

So why bother understanding It?

It’s tempting to think that being good at using AI is enough. And in many cases, it is. But for developers, there’s real value in understanding how these models work – even if only at a foundational level.

• How does a transformer work?
• What is a token, really – and how will the number of tokens affect your bill when you're charged per token?
• What does it mean that the model only sees previous tokens?
• What happens when we “fine-tune”?
• What is an attention mechanism? 

Benefits of understanding how an LLM works

With a better grasp of the underlying mechanisms, it becomes easier to evaluate where LLMs can be effectively applied—and where they shouldn’t. This can help avoid both underestimating the models’ capabilities and overhyping their potential.

Understanding concepts like tokenization and context windows also enables:

• Writing more concise and effective prompts

• Estimating API usage costs more accurately

• Choosing the right strategy—whether that’s prompt engineering, retrieval-augmented generation (RAG), or fine-tuning

Insight into how models predict the next word can also explain why they sometimes provide confident, yet incorrect, answers. The model simply doesn’t know when it’s lying. This helps you build in validation before hallucinations reach production.

Many LLM tools and APIs expose parameters like temperature and top-p. Without knowing the math behind them (logits and probability distributions), tuning these settings can feel like guesswork. With understanding, developers can guide outputs more reliably—ranging from precise and deterministic to creative and exploratory.

This isn’t just about nerdy curiosity (although there’s plenty of that too). It’s about using these tools with confidence and awareness. Understanding how LLMs work makes it easier to spot limitations, build better prompts, reduce failure modes, and make stronger architectural decisions when integrating language models into systems.

So even if you’re not planning to build your own LLM anytime soon, having a mental model of how they function gives developers a significant edge.

Roger Gullhaug, Director of Development and Operations at RamBase

Do we really understand how AI works?

A common claim is: “Nobody really knows how these models work.” But is that true?

Modern large language models are built using well-understood principles—transformer architectures, gradient descent, backpropagation, and massive text corpora. The processes behind how they learn and operate are based on tangible mechanisms like linear algebra, probability distributions, and neural network design.

What remains challenging is interpreting exactly why a model gives a specific output. The knowledge it learns is distributed across billions of parameters. That means we can’t isolate specific facts or behaviors within a single node. But this doesn’t mean the models are black boxes—it just means the complexity is spread out and hard to untangle on a micro level.

So while it may not be possible to explain every individual decision a model makes, the architecture, training processes, and behavioral patterns are broadly understood.

In summary

Understanding how large language models work isn’t just about curiosity. It’s part of being a competent developer in the face of a technology that’s already changing how software is built, tested, and used. Having a solid mental model of how LLMs function makes it easier to navigate their limitations, leverage their strengths, and build smarter solutions.

Earlier this year, I decided to take a deep dive into how large language models actually work. That journey led me to write a short book: The Inner Workings of Large Language Models – How Neural Networks Learn Language. It’s a free e-book, written for everyone with a technical curiosity who wants to go beyond surface-level usage and really understand what’s going on under the hood.

I hope you enjoy reading it – and good luck with your next prompt!