Large Language Models (LLMs) are changing how we build software, and not in that vague "AI is the future" way that people have been saying for years.
The tool that transforms a Figma design into a fully responsive website? The assistant that writes your unit tests while you focus on core functionality? These aren't hypothetical scenarios; they're happening right now.
The best part is that adding LLMs to your codebase has become surprisingly straightforward. But even with this simplicity, understanding how these models actually work is crucial for using them effectively.
This guide will explain what Large Language Models actually are, how they work behind the scenes, and what you should consider when adding AI to your web projects.
Let's build our understanding step by step, starting with the fundamentals and working our way up to large language models.
When people talk about AI and machine learning, you'll hear the term "model" thrown around constantly.
A model is basically a mathematical function that transforms inputs into outputs. But unlike traditional programming, where you'd write explicit rules, models learn patterns from data.
Here's what that looks like in practice:
- Feed a model tons of cat and dog pictures, and it learns to tell them apart (classification model)
- Show it housing data, and it figures out how square footage affects the price (regression model)
- Give it your browsing history, and it starts guessing what you might buy next (recommendation model)
The process of training models is structured: you show the model examples, measure its error using mathematical functions (like "how far off was this prediction?"), and then let it adjust its internal parameters to reduce that error. The model uses optimization algorithms to systematically improve its accuracy with each round. Do this thousands or millions of times, and eventually, it gets pretty good at complex tasks.
What makes models powerful isn't fancy math or algorithms (though those help). It's the data. Models are only as good as what they're trained on. Take a housing price predictor, for example. If you only train it on housing prices in a luxurious neighborhood, it's gonna be totally lost when estimating values in rural villages—no algorithm, however sophisticated, can extract patterns from data it hasn't seen before.
In the end, a model is just a function that makes predictions based on patterns it's seen before. It's not magic, just math at scale.
A language model is exactly what it sounds like—a model that works with human language. At its most basic level, it tries to predict what word will come next in a sequence.
When you type "The coffee is...", a language model calculates probabilities. It might think "hot" has a 30% chance of being next, "delicious" 15%, and "programming" basically 0%.
The first language models were pretty basic:
- N-gram models just counted how often word sequences appeared together
- Markov chains looked at the last couple of words to guess the next one
- RNNs (Recurrent Neural Networks) tried to remember context from earlier in the text but weren't great at it
These older models were useful for some tasks but had major limitations. They'd quickly lose track of what was being discussed if the relevant information wasn't within their context window - which ranged from just a few words for n-grams to around 100 words for basic RNNs.
Modern language models are much more sophisticated, but the core idea remains the same: predict what text should come next based on patterns learned from data.
A large language model (LLM) is, as the name suggests, a language model that's been scaled up dramatically in three key ways:
- Data: They're trained on vast amounts of textual data—think hundreds of billions of sentences from books, articles, websites, code repositories, and more
- Parameters: They have billions or trillions of adjustable internal values that determine how inputs are processed
- Computation: They need absurd amounts of computing power to train—the kind only big tech companies can typically afford
What's fascinating is that once you scale these models big enough and combine them with advanced architectures, they develop capabilities nobody explicitly programmed. They don't just get better at predicting the next word—they can:
- Generate human-like text that is coherent and lengthy
- Follow complex instructions
- Break down problems step-by-step
- Write working software code
- Understand different contexts and tones in natural language
- Answer questions using information they've absorbed
This emergent behavior surprised even the researchers who built LLMs. Scale unlocks capabilities that smaller models just don't have.
When you feed text into an LLM, it first breaks everything down into "tokens," which can be words, parts of words, or even individual characters. It then processes these tokens through its neural network to predict what should come next.
However, the real breakthrough behind modern LLMs is the transformer architecture (what the "T" in ChatGPT stands for), which completely changed how models process language.
Instead of processing text one word at a time like older models, transformers can look at entire passages at once and determine which parts should influence each other.
The most important part of transformers is the "attention" mechanism, which allows the model to:
- Process text in parallel rather than word-by-word
- Consider relationships between words regardless of how far apart they are
- Weigh the importance of different words when generating each piece of output
Under the hood, LLMs are still doing the same basic job as simpler language models—predicting what comes next—but their scale and architecture make them capable of so much more.
The transformer model architecture is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism.
So, how do these large language models actually learn? It's just a whole lot of math, data, and computation.
The model learns patterns from training data consisting of trillions of words through unsupervised learning, without requiring explicit programming of linguistic rules.
LLM training typically happens in a few stages:
- Pre-training: This is the foundation stage where the model learns language by predicting missing words from massive text datasets.
- Fine-tuning: After pre-training, the model gets more specialized training:
- RLHF (Reinforcement Learning from Human Feedback): This is where models like ChatGPT get their polish:
The cool thing about this approach is that we never explicitly program rules of language or facts into the model. It learns patterns from data and then refines itself based on what humans prefer.
At Builder.io, we've spent years building tools that connect design and development, and we've learned valuable lessons about effectively leveraging LLMs.
With Visual Copilot, our AI-powered Figma-to-code toolchain, we've trained specialized AI models with over 2 million data points that transform Figma designs into clean, responsive code.
We don't just use a single AI model for this—that approach tends to fall apart in the real world. Instead, we've built a pipeline of specialized systems:
- First, a model transforms flat design structures into proper code hierarchies
- Then, our open-source Mitosis compiler adapts this structure to whatever framework you're using
- Finally, a fine-tuned LLM polishes the code to match your team's specific coding style and standards
This has dramatically cut down the time developers spend translating designs into actual working code, allowing them to focus on the stuff that matters: building great user experiences and working on the core functionality of their apps.
Working with LLMs in production has taught us that the difference between a mind-blowing AI feature and a frustrating one often comes down to the integration details most people overlook.
So how do you actually add LLMs to your web app? You could go down a rabbit hole, but I want to focus on the fundamentals: how to connect to these models, handle their responses, and not break the bank while doing it.
The most common way to integrate LLMs into web applications is through API calls to hosted models. Services like OpenAI, Anthropic, Cohere, and others provide API endpoints that accept your prompts and return responses.
Here's a basic example of using fetch to call an LLM API:
async function generateContent(prompt) {
const response = await fetch('https://api.llmprovider.com/generate', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${API_KEY}`
},
body: JSON.stringify({
prompt: prompt,
max_tokens: 8192,
temperature: 0.7
})
});
const data = await response.json();
return data.text;
}The key parameters typically include:
- Prompt: What do you want the model to respond to
- Temperature: How random/creative the output should be (0.0 = deterministic, 1.0 = creative)
- Max tokens: How long the response can be
- Model: Which model to use (affects quality, speed, and cost)
When implementing LLMs in user-facing web applications, consider these best practices:
- Handle loading states - LLM calls can take seconds, not milliseconds - show a spinner or typing animation
- Implement streaming - Most providers support streaming responses so you can show text as it's generated
- Add retry logic - LLM services can hit rate limits or have outages, so build in retry mechanisms
- Consider moderation - If users can input their own prompts, you may need filtering to prevent misuse
- Design for variability - No matter what temperature you use, responses will vary each time
On the server side, consider these approaches:
- Asynchronous processing - For non-interactive uses, process requests in the background
- Caching common responses - Store responses to common prompts to save money and improve speed
- Rate limiting - Prevent abuse by implementing rate limits for LLM-based features
- Prompt engineering - The quality of your prompt directly affects the results
- Validation and post-processing - Validate and clean LLM outputs before using them
LLMs are powerful but can get expensive quickly:
- Token optimization: Every token (roughly 4 characters) costs money, with input and output tokens typically priced differently – so be strategic with prompt length and expected response size
- Model selection: Use smaller models for simpler tasks
- Batching: Combine multiple requests when possible
- Hybrid approaches: Use LLMs for what they're good at and traditional code for the rest
You've got a few options depending on your needs:
- Managed API services: OpenAI, Anthropic, and Cohere provide simple REST APIs with comprehensive documentation
- AI SDK: Adds a nice layer of abstraction and type safety if that's your thing
Finding the right balance between these factors takes experimentation, but when done right, you can build features that would have been impossible just a few years ago.
LLMs are genuinely changing how we build software, but in more practical and immediate ways than the hype might suggest. You don't need a PhD, technical expertise or massive infrastructure to start using them effectively in your projects.
The learning curve exists, but it's more like learning a new framework than mastering quantum physics. Start with small experiments, try out the tools I've mentioned, and build from there.
What's exciting isn't just what these models can do today but how quickly they're evolving. So give it a shot—you might be surprised at how quickly you can go from curiosity to shipping features powered by LLMs.
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