Tools: Ultrathink: Tell Claude Exactly How Hard to Think About Your Problem

The Problem ## The Solution ## How to Use It ## What This Looks Like in Practice ## Placement Is Flexible ## Pro Tips ## Real-World Use Case ## Conclusion Sometimes you need a quick answer. Sometimes you need Claude to really chew on it. Now you control which is which. Here's a truth about AI that nobody talks about enough: not every question deserves the same amount of computational thought. Asking "what's the syntax for a for loop in Python" shouldn't trigger the same deep reasoning as "help me architect a distributed system for 10 million concurrent users." Yet most AI interactions treat every prompt the same. You get what you get, with no control over the depth of reasoning. Until now. Today we're exploring one of Claude Code's most powerful features: the ability to explicitly control how deeply Claude thinks before responding. Welcome to the world of Ultrathink. Let's be honest about how we use AI coding assistants. Our requests fall into roughly three categories: Quick lookups: "What's the Python syntax for list comprehension?" You need an answer in 2 seconds. Deep reasoning would be overkill. Moderate problems: "Help me refactor this function to be more readable." Some thought required, but not a dissertation. Complex challenges: "I'm seeing intermittent failures in production. Here are the logs, the code, and our infrastructure setup. Help me find the root cause." This needs serious reasoning. The problem? Without explicit control, you're leaving the thinking depth to chance. Sometimes Claude blazes through something that deserved more thought. Sometimes it over-thinks a simple question. You're not in control. And for complex problems, there's a real cost to shallow thinking. You might get a surface-level answer that misses the nuance. You end up asking follow-up after follow-up, trying to guide Claude toward the insight you needed from the start. Claude Code introduces thinking keywords that give you explicit control over reasoning depth. Just include these magic words anywhere in your prompt: "think" — Standard Extended Thinking (4K tokens) This allocates 4,000 tokens of internal reasoning before Claude responds. Good for moderate complexity problems where you want more than a snap judgment. "think hard" — Deep Thinking (10K tokens) 10,000 tokens of reasoning. Use this when you need Claude to seriously evaluate multiple options, consider edge cases, and provide a well-reasoned recommendation. "ultrathink" — Maximum Depth (31,999 tokens) This is the big one. Nearly 32,000 tokens of internal reasoning before you see a response. Claude will explore multiple approaches, argue with itself, backtrack, reconsider, and synthesize insights that surface-level thinking would miss. Here's a real example. Let's say you're debugging a complex issue: With ultrathink engaged, Claude will: You get an answer that reflects actual deep thought, not a quick pattern match. The magic words work anywhere in your prompt: 🎯 Tip 1: Match thinking depth to problem complexity. Don't ultrathink "how do I exit vim" — you're wasting time. Do ultrathink "how should I architect this microservice." 🎯 Tip 2: Ultrathink is perfect for code reviews. Paste a PR diff and say "ultrathink through potential issues with this change." You'll get a review that rivals a senior engineer's. 🎯 Tip 3: Use it for architectural decisions. Before you commit to a technical direction, let Claude ultrathink through the implications. It's like having a senior architect on demand. 🎯 Tip 4: Combine with context. Ultrathink is most powerful when Claude has full context. Share relevant files, explain the constraints, describe the goals — then unleash the deep thinking. Marcus is a tech lead making a critical decision: Should his team adopt a microservices architecture or stick with their modular monolith? In the old days, he'd ask Claude and get a generic "it depends" response listing pros and cons. Useful, but not decisive. Claude spends 32,000 tokens really thinking about this. The response includes: Marcus gets an answer that would have taken a consultant a week to produce. All because he told Claude to actually think. The ultrathink feature represents a fundamental shift in how we interact with AI. You're no longer a passive recipient of whatever thinking depth the model happens to apply. You're in control. Quick question? Just ask normally. Moderate complexity? Tell Claude to "think." Complex architectural decision? Ultrathink it. This isn't just about getting better answers. It's about efficiency. You stop wasting time on follow-up questions that wouldn't be necessary if Claude had thought deeper initially. You stop getting shallow answers to deep questions. Tomorrow in Day 13: What happens when you're mid-prompt and need to ask something else urgently? We're covering Prompt Stashing — like git stash, but for your thoughts. Never lose a draft again. 💾 Did ultrathink level up your Claude game? Share your experience and follow along for the rest of the 31 Days of Claude Code Features! Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: think about how I should structure this React component for maximum reusability Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: think about how I should structure this React component for maximum reusability CODE_BLOCK: think about how I should structure this React component for maximum reusability CODE_BLOCK: think hard about the trade-offs between these three database architectures for our use case Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: think hard about the trade-offs between these three database architectures for our use case CODE_BLOCK: think hard about the trade-offs between these three database architectures for our use case CODE_BLOCK: ultrathink through this distributed systems design and identify potential failure modes Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ultrathink through this distributed systems design and identify potential failure modes CODE_BLOCK: ultrathink through this distributed systems design and identify potential failure modes CODE_BLOCK: I'm seeing race conditions in our payment processing. Here's the relevant code: [code block] The error appears intermittently, about 1 in 100 transactions. ultrathink through potential causes and solutions. Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: I'm seeing race conditions in our payment processing. Here's the relevant code: [code block] The error appears intermittently, about 1 in 100 transactions. ultrathink through potential causes and solutions. CODE_BLOCK: I'm seeing race conditions in our payment processing. Here's the relevant code: [code block] The error appears intermittently, about 1 in 100 transactions. ultrathink through potential causes and solutions. CODE_BLOCK: ultrathink — what's wrong with this architecture? Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ultrathink — what's wrong with this architecture? CODE_BLOCK: ultrathink — what's wrong with this architecture? CODE_BLOCK: Here's my code. Think hard about potential security vulnerabilities. Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Here's my code. Think hard about potential security vulnerabilities. CODE_BLOCK: Here's my code. Think hard about potential security vulnerabilities. CODE_BLOCK: I need to optimize this query. Think about it and suggest improvements. Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: I need to optimize this query. Think about it and suggest improvements. CODE_BLOCK: I need to optimize this query. Think about it and suggest improvements. CODE_BLOCK: ultrathink We're a team of 8 developers building a B2B SaaS product. Current state: modular monolith, 50K LOC, PostgreSQL, deployed on AWS ECS. We have 500 customers, growing 20% MoM. Pain points: deployments take 45 minutes, one team's changes often break another team's features, scaling specific features independently isn't possible. Question: Should we migrate to microservices? If yes, what's the migration strategy? If no, what should we do instead? Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: ultrathink We're a team of 8 developers building a B2B SaaS product. Current state: modular monolith, 50K LOC, PostgreSQL, deployed on AWS ECS. We have 500 customers, growing 20% MoM. Pain points: deployments take 45 minutes, one team's changes often break another team's features, scaling specific features independently isn't possible. Question: Should we migrate to microservices? If yes, what's the migration strategy? If no, what should we do instead? CODE_BLOCK: ultrathink We're a team of 8 developers building a B2B SaaS product. Current state: modular monolith, 50K LOC, PostgreSQL, deployed on AWS ECS. We have 500 customers, growing 20% MoM. Pain points: deployments take 45 minutes, one team's changes often break another team's features, scaling specific features independently isn't possible. Question: Should we migrate to microservices? If yes, what's the migration strategy? If no, what should we do instead? - Analyze the code structure systematically - Consider multiple potential race condition sources - Think through the timing implications - Evaluate different locking strategies - Consider edge cases and failure modes - Synthesize a comprehensive response - Analysis of whether their pain points actually require microservices - Alternative solutions (better modular boundaries, feature flags, etc.) - If microservices: a phased migration strategy starting with the highest-pain areas - Team structure implications - Timeline estimates - Warning signs that they're moving too fast or too slow