You will understand what the ReAct agent loop is, why it exists, and how to build a tiny working version in Spring Boot — so an AI can reason and act in steps instead of guessing in one shot.
Picture this: a learner asks EngineerPrep's AI tutor, 'What is the time complexity of HashMap.get() in the worst case?' The tutor answers confidently: O(1). That's the average case. The worst case — when every key hashes to the same bucket, producing a single collision chain — is O(n). The tutor was wrong, and it had no idea. Your first fix is to add more detail to the prompt. You paste in some Java docs. The answer gets better, but now you're playing whack-a-mole: every new question type might be wrong in a new way. The real issue is the architecture. A single LLM call is like asking a colleague a question and hanging up before they can say 'let me check that.' They can only go with what's already in their head. What if, instead, the AI could think out loud , decide to look something up, read the result, and then answer? That's exactly what the ReAct loop gives you…
Think about how you solve a problem you've never seen before. You don't just blurt out an answer. You think about what you know, decide to look something up, read a result, think again, maybe search once more, and finally give an answer you're actually confident in. That back-and-forth — think, act, observe, think again — is exactly what the ReAct pattern is. ReAct stands for Reasoning + Acting . It's a loop where an AI: 1. Thinks about what it knows and what it still needs. 2. Acts by calling a tool — like a search, a database lookup, or a calculator. 3. Observes what the tool returned. 4. Repeats until it has enough to give a final answer. Each pass through the loop is called a step . The AI keeps stepping until it decides it's done. In EngineerPrep, this is how the AI tutor works for complex questions.…
Imagine a whiteboard with four boxes arranged in a circle, connected by arrows. Step 0 — The question arrives. A learner types: 'Explain HashMap worst-case complexity and show me where in the JDK source it matters.' The question lands in the top box, labelled Thought . Nothing has happened yet — the AI just received the question. Arrow → Thought (Step 1) The AI writes its first thought: 'I know the average case is O(1), but I'm less sure about the worst case. I should look up the JDK source to be precise.' This is pure reasoning — no answer yet, just a plan. Arrow → Act (Step 1) Because the thought ended with 'I should look up', the AI picks the searchLessons tool and fires it with the query 'HashMap bucket collision worst case JDK source'. The right box, labelled Act , lights up. One tool call goes out.…