Behind the Magic: How LLMs Work and Why It Matters

Exploring the mechanics of large language models to demystify AI and encourage responsible use.

The other day I saw a tweet by @HarperSCarroll explaining in a very simple language what a neural network is. I’ve been researching this and trying to understand, but when she put it as “complicated equations that transform input into output”, it really made sense.

Large Language Models (LLMs) are trained by providing the input and the expected output, and then having the machine figure out the algorithm required for the machine to get to the expected output most (95+%) of the time.

I think it is extremely important for us, as developers, to do the work to understand how LLMs work - this makes it easy to understand their limitations and pitfalls as we integrate them into our software. I highly recommend watching this video to visualize the process:

Understanding the mechanics behind LLMs helps demystify what might otherwise feel like magic. When we break it down, these models aren’t all-knowing entities—they’re systems designed to identify patterns in vast amounts of data and generate predictions based on those patterns. This perspective is crucial when designing AI-powered features because it reminds us to question the reliability of the outputs, especially in edge cases.

For instance, an LLM’s output isn’t a definitive answer but rather a statistically likely response based on the training data. If the training data is biased, incomplete, or outdated, the model's outputs will reflect those flaws. This is why we, as developers, need to remain critical thinkers and integrate checks and balances, such as validation layers or fallback mechanisms, into our AI implementations.

One common pitfall is over-relying on LLMs to perform tasks they weren’t designed for. Take reasoning, for example—LLMs can mimic reasoning to some extent, but they lack true understanding. They don’t “know” anything; they predict what comes next in a sequence based on probabilities. If we keep this in mind, we can set realistic expectations for what these tools can and cannot do.

Moreover, building trust in LLM outputs requires transparency with users. Showing confidence scores, providing citations (for retrieval-augmented setups), or explaining how a response was generated can go a long way in managing expectations and fostering user trust.

The video I linked earlier does a fantastic job of illustrating these concepts in action. It walks through how models like GPT process text and refine their predictions step-by-step. Visualizing the process can make the inner workings of LLMs feel much more approachable, even for those who aren’t deeply familiar with machine learning.

Ultimately, learning about LLMs isn’t just about being better developers—it’s about being responsible ones. AI isn’t a black box we can afford to use carelessly; it’s a tool that, when understood, can empower us to build better, more thoughtful applications. And when we know where the cracks might appear, we can proactively design systems that fill those gaps.