A large language model is a system trained on enormous amounts of text to predict what words naturally follow other words — and that simple idea is what gives it the ability to answer questions, explain code, and hold a conversation.
Picture your first week on the EngineerPrep team. Your job is to make the platform smart — it should explain Java concepts, answer follow-up questions, and adapt its tone to whoever is asking. You try the obvious approach: a lookup table. Store a question, store an answer. When a learner asks something, find the closest match and return it. It works for three questions. Then a learner asks the same thing in a different way. Then someone asks a follow-up. Then someone mixes two topics in one sentence. Your lookup table falls apart almost immediately. You need something that understands language — not just matches strings. But how do you build that? That's exactly what a large language model (LLM) is built to do. And once you understand the core idea, you'll see why EngineerPrep uses one at the center of almost every feature it ships.
Think about autocomplete on your phone. You type 'Happy birth' and your phone suggests 'day.' It learned that from seeing millions of messages. It doesn't know what a birthday is — it just knows that 'day' almost always follows 'Happy birth.' A large language model — LLM for short — is that same idea, scaled up dramatically. Instead of learning from a few thousand messages, it learned from billions of pages of text: books, articles, code, conversations, documentation. And instead of predicting one word at a time on a tiny keyboard, it predicts the next token (roughly a word or part of a word) in a long sequence. The result is a system that can complete any sequence of text in a way that sounds natural and contextually appropriate — including sequences that look like questions, instructions, or conversations.…
Step 1 — The learner types a question. A learner opens EngineerPrep and types: 'What is the difference between an interface and an abstract class in Java?' Nothing has happened yet. It's just text sitting in a browser. --- Step 2 — The question becomes a prompt. EngineerPrep's backend wraps that question in a prompt — the full block of text you send to the LLM. The prompt includes a system instruction ('You are a friendly Java mentor') and the learner's question. Think of the prompt as a letter you're handing to a very well-read assistant. --- Step 3 — The prompt travels to Amazon Bedrock. Spring Boot's LLM client sends the prompt over HTTPS to Amazon Bedrock, which forwards it to Claude. Claude is the LLM — the trained model running on Anthropic's infrastructure. --- Step 4 — Claude predicts, token by token. Claude starts generating a response. It doesn't retrieve a stored answer.…
This is a stripped-down version of the kind of service EngineerPrep uses for its AI tutor. We're using Spring AI — a Spring library that gives you a clean Java API for talking to LLMs — backed by Amazon Bedrock. Let's walk through it one piece at a time. The service class We annotate it with @Service so Spring manages it as a bean — meaning Spring creates one instance and injects it wherever it's needed. We use constructor injection (passing dependencies through the constructor) because it makes the dependencies explicit and easy to test. The ChatClient field ChatClient is Spring AI's main interface for talking to an LLM. You point it at a model (Claude, in this case) and call it with text. Spring AI handles the HTTP call to Bedrock for you. The explain method This is the method the REST controller calls when a learner asks a question. It takes the learner's raw question as a String .…