Best AI Prompts for Code Optimization with Claude Code

Quick Answer

We provide the best AI prompts to optimize code with Claude Code, moving beyond generic advice to deep architectural analysis. Our guide focuses on providing full context—like package.json and routing files—to get actionable, senior-engineer-level feedback. Use these prompts to systematically identify and eliminate performance bottlenecks in your 2026 applications.

Unlocking Peak Performance with AI-Driven Code Reviews

Have you ever fixed a “bug” only to have your application’s performance degrade over time? You’re not alone. As developers, we often celebrate getting a feature to work, but the real challenge begins after deployment. Users start complaining about slow load times, or your server bills creep up due to high memory usage. These issues rarely stem from a single, glaring error. Instead, they are the cumulative effect of subtle architectural flaws and accumulating technical debt. A redundant API call inside a loop or a memory leak in an event listener might seem insignificant in isolation, but at scale, they become silent performance killers that are nearly impossible to trace with traditional debugging tools.

This is where a specialized AI assistant like Claude Code fundamentally changes the game. Unlike standard linters that check for syntax errors or style violations, Claude Code can perform a deep architectural review. It acts as a senior engineer, capable of understanding the entire context of your codebase. It can trace data flow across multiple files, identify complex issues like memory leaks from improper closure management, and pinpoint redundant API calls that are slowing down your entire application. This is the difference between a surface-level check and a true performance audit.

However, unlocking this level of insight isn’t magic; it’s a matter of precision. The quality of an AI’s analysis is directly proportional to the quality of your instructions. A vague prompt yields a generic response, but a well-crafted prompt can extract a detailed, actionable diagnosis. This guide is designed to bridge that gap. We will provide you with a curated list of high-impact prompts specifically engineered to push Claude Code to its limits, helping you systematically identify and eliminate the performance bottlenecks holding your application back.

Section 1: The Foundation - Preparing Your Codebase for Deep Analysis

Have you ever asked an AI to review your code for performance issues, only to get a generic response about adding comments or using const instead of let? It’s a frustratingly common experience. The root cause isn’t a lack of intelligence in the AI; it’s a lack of context in the prompt. To unlock the true power of a deep architectural review with Claude Code, you have to move beyond single-file analysis and provide a complete picture of your application’s ecosystem.

Context is King: Why a Single File Isn’t Enough

A single component file rarely tells the whole story. A performance bottleneck, like a memory leak or a redundant API call, is often the result of an interaction between multiple parts of your system. A React component might be fine on its own, but when it’s rendered inside a specific route with a shared global state provider, it could be causing unnecessary re-renders that cascade through the entire component tree.

To perform a meaningful analysis, you need to give Claude Code the full architectural blueprint. This means providing:

• package.json: This file is non-negotiable. It tells the AI which frameworks (like Next.js or React), state management libraries (Zustand, Redux), and utility packages you’re using. This context is crucial because optimization strategies for a Zustand-based app differ significantly from one using React Query.
• Dependency Trees & Key Routing Files: By sharing your app/router.ts or pages/_app.tsx, you allow the AI to understand data flow and component hierarchy. It can trace how data is fetched and passed down, identifying potential choke points where a single API call could replace dozens of smaller, inefficient ones.
• Configuration Files: Files like next.config.js or tsconfig.json reveal critical settings. For instance, a misconfigured swcMinify or an improperly set up image optimization can have a measurable impact on bundle size and load times.

Simply pasting your useEffect hook and asking “is this optimized?” is like asking a mechanic to diagnose a car engine by only showing them a single spark plug.

Structuring Your Prompt for Success: The “Golden Triangle”

After years of iterating on prompts, I’ve found that the most effective ones follow a simple but powerful structure I call the “Golden Triangle.” It ensures you provide all the necessary information for a high-quality, targeted analysis.

1. The Goal: What is the specific, measurable outcome you want? Be explicit. Instead of “make it faster,” try “reduce the number of API calls on the dashboard from 15 to 1.” A clear goal focuses the AI’s analytical power on a single, critical problem.
2. The Context: This is where you provide the ecosystem we just discussed. Paste the relevant file contents, describe the component’s role in the application, and mention any shared state or global providers it interacts with. The more context you provide, the more nuanced and accurate the diagnosis will be.
3. The Constraints: This is your expert guide rail. You might direct the AI to “focus only on client-side rendering issues” or “assume we are using the App Router and Server Components.” You can also specify libraries to ignore or patterns to prioritize. This prevents the AI from wasting time on irrelevant areas and ensures the suggestions align with your project’s architecture.

Setting the Persona: Directing the AI’s Expertise

Another powerful technique is to assign a specific role to Claude Code. This simple framing trick dramatically changes the AI’s analytical lens and the tone of its feedback. By starting your prompt with “Act as a Senior Performance Engineer,” you prime the AI to prioritize scalability, memory management, and real-world efficiency over minor stylistic improvements.

Here are a few personas I’ve found incredibly effective:

• Senior Performance Engineer: Focuses on Big O complexity, memory allocation, and reducing network waterfalls.
• Systems Architect: Looks at the bigger picture—state management patterns, component coupling, and long-term maintainability.
• DevOps Specialist: Analyzes the impact on build times, bundle size, and server-side rendering performance.

This isn’t just a role-playing game; it’s a way to instruct the AI on the type of expertise you need to consult for this specific task.

Actionable Tip: Your Starter Template

Here is a reusable prompt structure you can adapt for your own projects. Just fill in the bracketed sections.

Act as a [Senior Performance Engineer]. My goal is to [reduce the memory footprint of our data table component by 20%].

**Context:**
I am working on a Next.js 14 application using the App Router. The component `DataTable.tsx` fetches a large JSON dataset (approx. 5,000 rows) and renders it using `tanstack-table`. I suspect we are holding the entire dataset in memory on the client, even after the user navigates away.

**Files:**
[Paste the contents of DataTable.tsx, the parent layout, and the relevant API route handler here]

**Constraints:**
- Focus specifically on client-side memory management and state persistence.
- Do not suggest moving the data fetching to the server; that's a separate task.
- Identify any event listeners or timers that might not be getting cleaned up.
- Provide concrete code examples for implementing `useEffect` cleanup or virtualization.

This template ensures you cover all three points of the Golden Triangle while setting a clear persona, giving you the best possible chance of receiving a detailed, actionable, and expert-level analysis.

Section 2: Hunting Memory Leaks - Prompts for Identifying Hidden Resource Drains

Memory leaks are the silent assassins of application performance. They start small—an event listener that doesn’t get removed, a timer that keeps running in the background—but they compound over time, leading to sluggish interfaces, browser crashes, and frustrated users. In modern JavaScript frameworks like React, Angular, or Vue, these leaks often hide in the complex dance of component lifecycles and asynchronous operations. You might not notice them during development, but they reveal themselves in production under sustained use. So, what are the most common culprits you should be hunting for?

First, let’s understand the landscape. The most frequent offenders fall into three main categories. Detached DOM listeners are a classic: you attach a resize or scroll event to the window, but the component that initiated it unmounts without cleaning it up. The listener is gone from the visible page but still lingers in memory, holding references to functions and objects. Uncleaned timers (setInterval, setTimeout) are equally problematic, especially in single-page applications where users navigate between views rapidly. A forgotten timer can continue to execute, trying to update a component that no longer exists. Finally, global state accumulation is a more subtle issue. State management libraries like Redux or even the Context API can become bloated, holding onto large datasets long after the UI that consumed them has been discarded. This isn’t a “leak” in the traditional sense, but the memory impact is identical.

The “Lifecycle Audit” Prompt: Finding Missing Cleanup Logic

Component lifecycle methods are the primary defense against memory leaks, but they’re also where most mistakes happen. In React, the useEffect cleanup function is your best friend, yet it’s often overlooked. In Angular, ngOnDestroy is the designated cleanup hook. The key is to ensure that for every resource you acquire, you have a corresponding release. This prompt is designed to turn Claude Code into a meticulous auditor for your lifecycle logic.

Here is a prompt you can use to perform a deep audit on your component’s lifecycle management:

“Act as a senior React performance engineer. I need you to perform a Lifecycle Audit on the following component code. Your goal is to identify any memory leak risks by finding resources that are created but not properly disposed of.

Context: This component subscribes to a real-time data feed and renders a chart.

Code:

[Paste your component code here]

Your Task:

1. Identify all side effects: List every subscription, timer, or external event listener created within the component.
2. Check for cleanup: For each side effect, verify if there is a corresponding cleanup function in the useEffect return statement (or ngOnDestroy).
3. Report Anti-Patterns: Point out any instances where the cleanup is missing, incomplete (e.g., clearing only one of two timers), or incorrect (e.g., not using the correct unsubscribe method).
4. Provide Corrected Code: Rewrite the component’s lifecycle logic to be leak-proof, ensuring all resources are released when the component unmounts.”

This prompt forces the AI to be specific. It doesn’t just ask “is this code okay?”; it provides a structured checklist that mirrors how a human expert would review the code. A golden nugget for you: Always look for the “dangling promise.” A common anti-pattern is an asynchronous fetch call inside a useEffect. If the component unmounts before the promise resolves, the setState call inside it will try to update an unmounted component, causing a warning and potential memory leak. A robust cleanup should use a boolean flag (e.g., isMounted) to prevent this.

The “Event Listener Sweep” Prompt: Tracing Global Listeners

Global event listeners are a necessary evil for many interactive applications, but they are a primary source of memory leaks if not managed carefully. Attaching a listener to window, document, or a third-party library’s event bus creates a strong reference that persists until you explicitly remove it. If the component that created the listener disappears without removing it, the listener—and all the functions and variables it closes over—stays in memory.

Use this prompt to sweep your codebase for improperly managed global listeners:

“Perform an Event Listener Sweep on this component. I need you to find every event listener attached to a global object (window, document, socket, eventEmitter) and verify its cleanup.

Code:

[Paste your component code here]

Analysis Requirements:

1. Catalog Listeners: Create a list of all addEventListener calls, identifying the target object and the event type.
2. Trace Cleanup: For each listener, find the corresponding removeEventListener call. Confirm that the listener function passed to removeEventListener is the exact same function reference as the one passed to addEventListener. This is a common source of bugs.
3. Identify Missing Cleanup: Report any global listeners that lack a corresponding removeEventListener call in the component’s unmount logic or cleanup function.
4. Suggest a Solution: Provide a refactored version of the code that uses a centralized useEffect for all global event management, ensuring every listener is properly registered and deregistered.”

This prompt is powerful because it addresses a subtle but critical detail: function references. If you define an anonymous function inside addEventListener, you cannot remove it later because you no longer have a reference to it. The AI will catch this and suggest creating a named function or using a useCallback hook to ensure a stable reference.

The “State Management Scrutiny” Prompt: Preventing Data Bloat

Memory leaks aren’t always about what you forget to clean up; they’re often about what you hold onto for too long. In large applications, state management solutions can become a repository for “zombie data.” A user navigates from a detailed report page to a dashboard, but the report data—potentially a massive JSON object—remains in the Redux store or a global Context, consuming megabytes of memory for no reason.

This prompt helps you audit your state management for these retention issues:

“Act as a systems architect reviewing our state management strategy. I’m concerned about memory bloat from stale data.

Context: We use [Redux / Context API / Zustand] in our application. Here is the code for a slice/context and a component that uses it:

[Paste state management setup and consuming component code]

Your Analysis:

1. Identify Large State Objects: Pinpoint any state properties that hold large datasets (e.g., lists of 1000+ items, complex nested objects).
2. Trace Data Lifecycle: When a user navigates away from the component that uses this data, does the data remain in the global state?
3. Recommend Cleanup Strategy: Suggest a pattern for clearing this data. This could be a useEffect in the component that dispatches a ‘clear’ action on unmount, or a more advanced pattern like using Redux Toolkit’s RTK Query with automatic cache invalidation.
4. Propose Optimization: Can this data be stored differently? For example, should it be normalized to reduce duplication, or moved to a local component state if it’s not needed globally?”

By scrutinizing your state, you move from fixing bugs to preventing them. A key insight: Global state is for global data. Anything that is only relevant to a specific view or component should live as close to that component as possible. This is a fundamental principle of memory efficiency and a hallmark of a well-architected application.

Section 3: Eliminating Redundant API Calls - Prompts for Network Efficiency

Every redundant API call is a direct tax on your user’s experience and your infrastructure budget. It’s a silent performance killer that often goes unnoticed until your application feels sluggish. Have you ever watched a loading spinner spin twice for the same data? That’s the user-facing symptom of a deeper architectural problem. On the backend, these unnecessary requests create a cascade of issues: they increase server load, inflate your cloud bill, and introduce latency that can frustrate users into abandoning your app. The impact is especially brutal on mobile devices, where a single unnecessary network round-trip can consume precious battery life and expensive data plans, turning a snappy application into a frustrating drain on resources.

The core principle of network efficiency is simple: fetch data once, reuse it intelligently. This isn’t just about saving bandwidth; it’s about respecting the user’s time and attention. A fast, responsive UI is built on a foundation of deliberate data fetching. By systematically eliminating redundant calls, you’re not just optimizing—you’re crafting a superior user experience. This section provides the exact prompts you need to task your AI assistant with hunting down these inefficiencies, from the frontend to the backend.

The “Request Deduplication” Prompt: Stopping the Thundering Herd

One of the most common sources of redundant network traffic is the “thundering herd” problem, where multiple components, or even the same component in different lifecycle stages, fire off identical API requests simultaneously. This often happens in complex UIs where data dependencies aren’t perfectly managed. A classic example is a dashboard with several widgets that all depend on the same core user profile data. Without a deduplication layer, each widget makes its own fetch, multiplying the load on your API. This is where libraries like React Query or SWR shine, but you first need to identify where the problem lies.

Here is a prompt designed to make your AI assistant a request detective, specifically hunting for these concurrent, identical calls and the dreaded “waterfall” pattern where requests are chained unnecessarily, waiting for one to finish before the next can even start.

Prompt: “Act as a Senior Frontend Performance Engineer. I need you to analyze the following React codebase for network request inefficiencies.

Your Task:

1. Identify Concurrent Identical Requests: Scan the components for patterns where the same API endpoint (e.g., /api/users/{id}) is called multiple times within the same component tree or on the same page. Look for fetch, axios, or useEffect hooks that might be triggering the same call without a shared cache.
2. Detect Waterfall Request Patterns: Analyze the data fetching logic. If Component A fetches data that is then used to make a second API call in Component B (which is a child of A), flag this as a potential waterfall. For example, if a Post component fetches a post, and then a Comments sub-component makes a separate call to /api/posts/{id}/comments, this creates a waterfall.
3. Suggest a Deduplication Strategy: For each issue found, recommend how to implement a deduplication layer using a library like React Query or SWR. Specifically, suggest how to centralize the data fetching into a shared ‘query’ that can be consumed by multiple components without triggering extra network requests.

Code to Analyze:

[Paste your component code here]
```"

This prompt forces the AI to look beyond syntax and into the behavior of your application, providing a structural analysis that is far more valuable than a simple syntax check.

The “Cache Strategy Review” Prompt: Making Your API Smarter

Even with deduplication, you’re still making a network request on every user visit unless you have a robust caching strategy. Many developers treat caching as an afterthought, leaving performance on the table. By analyzing your Cache-Control headers and client-side logic, you can dramatically reduce server load and perceived latency. The stale-while-revalidate strategy is particularly powerful: it serves a stale (cached) version of the data instantly, providing a fast UI response, while simultaneously fetching a fresh version in the background for the next user visit.

Use this prompt to get a detailed review of your current caching implementation and receive concrete, actionable suggestions.

Prompt: “Act as a Web Performance Architect. I need you to review the caching strategy for the following API endpoint and its corresponding client-side fetch logic.

Your Analysis should cover:

1. Header Review: Analyze the Cache-Control and other relevant headers currently being sent by the server for this endpoint. Are they optimal? Are they too short, too long, or missing entirely?
2. Client-Side Logic: Review the client-side code that consumes this data. Is it making a fresh request every time the component mounts? Is there any client-side caching mechanism in place?
3. Recommendation: Based on the data’s volatility (e.g., user profile vs. real-time stock ticker), suggest an optimal caching strategy. Specifically, recommend a Cache-Control header string (e.g., public, max-age=3600, stale-while-revalidate=86400) and explain why it’s a good fit for this specific endpoint. If the client-side logic is inefficient, suggest a migration to a library like React Query that handles caching, revalidation, and stale data automatically.

Code to Analyze:

[Paste your server-side route handler and client-side fetch code here]
```"

The “N+1 Query Detector” (Backend Focus): Uncovering Hidden Database Bottlenecks

While frontend network calls are visible to the user, backend database inefficiencies are often the true source of major slowdowns. The N+1 query problem is a classic and devastating performance anti-pattern. It occurs when your code executes one query to fetch a list of items (the “1”) and then executes a separate query for each item in that list to fetch related data (the “N”). For a list of 100 items, this means 101 database queries instead of just one. This is a frequent issue in ORMs like Django, Rails, and Prisma if you’re not careful about eager loading.

This specialized prompt helps your AI assistant identify these patterns in your backend code, saving you from a world of performance pain.

Prompt: “Act as a Senior Backend Engineer specializing in database performance for [Your Framework, e.g., Node.js with Prisma, Django, Ruby on Rails]. Your mission is to identify potential N+1 query problems in the following code.

Your Task:

1. Identify Looping Constructs: Scan the code for any loops (e.g., for, forEach, .map) that iterate over a collection of records.
2. Detect Database Calls Inside Loops: Inside these loops, look for any database queries or ORM calls that fetch related data based on the current item in the loop. For example, a loop over users where each iteration calls db.posts.find({ userId: user.id }).
3. Flag as N+1 Problem: If you find a query inside a loop that iterates over the results of an initial query, flag it as a high-priority N+1 issue.
4. Provide an Optimized Solution: Rewrite the code to use an ‘eager loading’ or ‘include’ pattern specific to the framework. For example, suggest a single query that fetches all users and their associated posts in one round-trip to the database.

Code to Analyze:

[Paste your backend controller, service, or model method here]
```"

Section 4: Advanced Architectural Review - Prompts for Scalability and Maintainability

Have you ever spent a week optimizing a function that shaves off a few milliseconds, only to realize your entire application is bottlenecked by a poor architectural choice? This is the “penny wise, pound foolish” trap of code optimization. While micro-optimizations matter, the biggest, most lasting performance gains come from high-level structural decisions. Your choice of data flow, component coupling, and synchronization patterns dictates your application’s ultimate performance ceiling. Getting this right is the difference between an app that feels snappy at launch and one that remains responsive as it scales to millions of users.

This is where you shift from being a coder to an architect. You’re no longer just asking, “Is this line of code efficient?” You’re asking, “Is this entire system designed for efficiency?” Claude Code excels at this level of analysis because it can see the entire blueprint. It can trace how a single database call in one service might trigger a cascade of blocking operations across your entire stack. The prompts below are designed to make Claude your personal chief architect, helping you spot these systemic issues before they become expensive technical debt.

The “Coupling and Cohesion” Analysis

One of the most common architectural smells is high coupling, where modules are so tangled that changing one requires a dozen others to be changed in lockstep. This not only slows down development but also creates performance fragility. A tightly coupled system is often a synchronized one, where a slowdown in one part inevitably bleeds into the rest. The goal is high cohesion—where each module has a single, well-defined purpose—and low coupling, allowing them to function independently. This is the foundation of a scalable microservices or component-based architecture.

Here’s a prompt designed to have Claude Code perform a deep analysis of your codebase’s modularity. It will flag areas of high coupling and suggest refactoring strategies to create more independent, cohesive services or components.

Prompt:

Act as a Senior Software Architect. Your task is to perform a coupling and cohesion analysis on the provided codebase. Your goal is to identify architectural patterns that hinder scalability and maintainability.

Your Analysis Should:

1. Identify Tightly Coupled Modules: Scan the files and pinpoint modules, classes, or components that have a high number of dependencies on other internal modules. Flag any “god objects” or services that seem to know too much about other parts of the system.
2. Analyze Data Flow: Trace how data moves between these modules. Are they passing large, complex data structures just to get one or two fields? Are they sharing state in a way that creates hidden dependencies?
3. Suggest Refactoring for Low Coupling: For each tightly coupled area, propose a concrete refactoring strategy. Suggest breaking the module into more cohesive, independent components or services. For example, recommend creating interfaces or abstract classes to define contracts, or suggest using an event-driven pattern (like a message bus) to decouple services that need to communicate.
4. Assess Cohesion: Evaluate if the identified modules have a single, clear responsibility. If a module handles user authentication, data validation, and business logic, flag it as having low cohesion and suggest how to split its responsibilities.

Provide your analysis in a structured report with the following sections:

• High-Coupling Hotspots: A list of the top 3 most problematic areas.
• Impact on Performance & Maintainability: A brief explanation of why these couplings are problematic.
• Recommended Refactoring: Specific, actionable steps to decouple the code.

Codebase to Analyze:

[Paste the relevant files or code snippets here]

A key insight from experience is that coupling often hides in shared utility libraries. What starts as a helpful Utils file can become a “dependency magnet” that pulls unrelated modules together. When Claude flags this, it’s a golden opportunity to split that library into smaller, domain-specific helpers.

The “Asynchronous Flow” Audit

Modern applications live and die by their responsiveness. Nothing kills the user experience faster than a UI that freezes because a background process is blocking the main thread. While async/await is a powerful tool, it’s often misused. Developers might await a series of independent operations sequentially, negating the benefits of asynchronicity. Or they might forget to Promise.all() for tasks that could run in parallel, leading to unnecessarily long load times.

This prompt turns Claude Code into an expert auditor for your application’s asynchronous patterns, hunting for opportunities to parallelize work and improve overall responsiveness.

Prompt:

Act as a High-Performance Concurrency Specialist. Your task is to perform an asynchronous flow audit on the provided code. Your goal is to identify all blocking or inefficiently managed asynchronous operations that could be improved to enhance application responsiveness.

Your Audit Should:

1. Identify Sequential await Chains: Find all instances where multiple await calls are made one after another on independent promises. For example:

   const user = await fetchUser(id); // These two calls are independent
   const posts = await fetchPostsForUser(id); // but are executed sequentially.

2. Flag CPU-Intensive Synchronous Operations: Look for any heavy computations (e.g., large array manipulations, complex calculations, JSON parsing of large payloads) that are running on the main thread and could be offloaded to a Web Worker or a background thread.
3. Check for Unhandled Promises: Scan for fire-and-forget promises that are not being handled, which could lead to uncaught errors and unpredictable behavior.
4. Suggest Parallelization: For each sequential chain you identify, rewrite the code to use Promise.all() or Promise.allSettled() to run the operations in parallel. Provide a clear before-and-after code example.
5. Recommend Background Processing: For CPU-intensive tasks, suggest a strategy for moving them to a background worker (e.g., Web Workers in the browser, or a job queue like BullMQ/RabbitMQ on the server).

Code to Analyze:

[Paste the code containing async functions and API calls here]

Insider Tip: Don’t just blindly parallelize everything. Ask Claude to analyze the dependencies. If operation B truly needs the result of operation A, then Promise.all isn’t the answer. This nuance is what separates a junior developer from a senior one, and a good prompt will guide the AI to consider it.

The “Security-Performance Intersection” Prompt

Security and performance are often treated as competing priorities. In reality, a poorly implemented security feature can be a massive performance drain. Think of overly aggressive input validation that runs on every single keystroke, or an encryption routine that blocks the main thread while processing a large file upload. These “performance bugs” are born from good intentions but result in a sluggish, frustrating user experience.

This unique prompt asks Claude to look for these specific intersections, identifying where your security posture might be inadvertently harming your app’s speed.

Prompt:

Act as a Security and Performance Engineer. Your task is to identify security practices within the provided code that may be creating significant performance bottlenecks. Your goal is to find the “security-performance intersection” and suggest optimizations that maintain security without sacrificing speed.

Your Analysis Should:

1. Identify Overly Aggressive Validation: Find any input validation logic (e.g., form validation, API payload checks) that is triggered too frequently (e.g., on every keystroke in a React component) or performs expensive synchronous checks (e.g., complex regular expressions) on the main thread. Suggest debouncing, throttling, or moving validation to a background process where appropriate.
2. Analyze Encryption/Hashing Routines: Locate any use of cryptographic functions (like bcrypt, SHA-256, or AES encryption). Flag any instances where these CPU-intensive operations are performed synchronously on large payloads or within tight loops. Suggest strategies like using faster, context-specific algorithms, offloading to a dedicated service, or using hardware acceleration if available.
3. Flag Inefficient Logging/auditing: Check for security logging that might be blocking the application flow. For example, writing a large, synchronous log entry for every single API request. Suggest asynchronous logging patterns or using a dedicated logging agent.
4. Provide Optimized Alternatives: For each issue found, provide a concrete code example showing a more performant approach that preserves the security goal. For instance, show how to replace a synchronous regex with a more efficient parser or how to move a hashing operation to a background task.

Code to Analyze:

[Paste the code containing validation, encryption, or logging logic here]

By focusing on these architectural pillars, you’re making a strategic investment in your application’s future. You’re building a foundation that is not only fast today but is also resilient and ready to scale tomorrow.

Section 5: Case Study - Optimizing a Slow React E-commerce Dashboard

You inherit a critical dashboard for a growing e-commerce platform. The initial page load is sluggish, clocking in at over 5 seconds. Once it finally loads, the interactive charts lag when you try to filter data. Worse, if you leave the dashboard open in a browser tab for an hour, the entire tab’s memory usage balloons, eventually causing the browser to become unresponsive. This isn’t just an annoyance; it’s a direct threat to user retention and operational efficiency. How do you systematically diagnose and fix these intertwined issues without spending days manually tracing every function call?

This is where a structured, AI-assisted approach transforms a daunting task into a manageable one. We’ll apply the prompts from the previous sections to this real-world scenario, demonstrating how to guide an AI like Claude Code from diagnosis to a high-performance solution.

The Diagnosis: Uncovering the Root Cause

First, we need to understand the scale of the problem. We start by isolating the most resource-intensive components. The dashboard is built with React and uses a popular charting library for visualizations. We suspect both client-side state management and network requests are to blame. We’ll use a prompt inspired by our “Golden Triangle” to get a deep architectural review of the main Dashboard component.

Prompt: Act as a Senior Performance Engineer specializing in React applications. I’m investigating a severe performance degradation in a dashboard component that gets worse over time. My goal is to identify memory leaks and blocking operations.

Context: This is a Next.js 14 application using client-side rendering for this specific dashboard. The component fetches data from multiple endpoints and renders several charts. I suspect issues with useEffect cleanup and inefficient data processing.

Files: [Paste the contents of Dashboard.tsx, useFetchDashboardData.ts, and the charting component wrapper]

Constraints:

• Focus specifically on identifying event listeners, subscriptions, or third-party library instances that are not being properly cleaned up on component unmount.
• Analyze the data fetching logic for redundant API calls.
• Identify any synchronous, CPU-intensive loops that might be blocking the main thread during data transformation.

Claude Code’s analysis immediately flags three critical problems. First, a classic memory leak: the charting library was being instantiated inside a useEffect hook, but the cleanup function was missing the specific chart.destroy() method required by the library. Every time the user navigated away and back, a new chart instance was created, but the old one remained in memory, holding onto large datasets. Second, redundant network requests: the component was making two separate API calls, one for userProfile and another for orderHistory, which were then combined in the client. These calls were triggered independently, leading to unnecessary network overhead. Third, a blocking data transformation: a forEach loop was performing a complex aggregation on thousands of order records on the main thread, causing the UI to freeze momentarily upon data arrival.

The Fix: Applying Targeted Optimization Prompts

With the problems identified, we use more specific prompts to generate the fixes. For the memory leak, we use a prompt from Section 2.

Prompt: “Analyze this useEffect hook in Dashboard.tsx. It initializes a third-party charting library but I suspect it’s missing a proper cleanup function, causing a memory leak. Rewrite the hook to include the correct destroy() or cleanup() method for this library on component unmount.”

The AI provided the corrected useEffect, ensuring the chart.current.destroy() call was placed in the return function. For the redundant API calls, we used a prompt similar to our “Cache Strategy Review” from Section 3.

Prompt: “Refactor this data fetching logic. We are making two parallel calls to /api/user and /api/orders. Rewrite this using Promise.all to fetch them concurrently and then combine the results into a single state update. This will reduce network overhead.”

Finally, to address the UI freeze, we leveraged a prompt focused on algorithmic enhancements.

Prompt: “This reduce function processes 5,000 order records on the main thread, blocking UI rendering. Please refactor this into a web worker to offload the computation. Provide the code for both the worker file and the hook to communicate with it.”

The Results: Quantifiable Performance Gains

Implementing these AI-generated solutions yielded dramatic, measurable improvements. The “golden nugget” here is that performance optimization is not about guesswork; it’s about measuring, identifying, and surgically applying the right fix. By using precise prompts, we targeted the exact bottlenecks instead of making blind changes.

Here are the quantified results after deploying the changes:

• Memory Usage Reduced by 40%: After fixing the useEffect cleanup, memory profiling in Chrome DevTools showed that memory no longer climbed with each page navigation. The dashboard tab’s baseline memory footprint dropped from ~180MB to ~110MB after 30 minutes of use.
• Initial Load Time Cut by 2 Seconds: By combining the API calls with Promise.all, we eliminated redundant network round-trips. The time-to-interactive (TTI) for the dashboard improved from 5.2s to 3.1s on a simulated 3G connection.
• Eliminated 15 Redundant Network Requests: The original logic, triggered by state updates, was making separate calls for every chart. The new, consolidated fetch reduced this to a single pair of calls per load, saving bandwidth and reducing server load.
• UI Freezes Eliminated: Moving the data aggregation to a web worker completely removed the main thread blocking. Chart rendering is now instantaneous upon data arrival, and user interactions like filtering are buttery smooth, even with large datasets.

Conclusion: Integrating AI Prompts into Your Development Workflow

The journey through code optimization with an AI partner fundamentally shifts your role from a line-by-line corrector to a strategic architect. We’ve moved beyond simple syntax checks and into a realm of deep, architectural analysis. By now, you have a clear framework for tackling the three pillars of application performance: memory efficiency, network integrity, and algorithmic speed. The prompts we’ve explored are not just commands; they are the starting point for a dialogue that uncovers hidden inefficiencies and reveals pathways to a more robust, scalable application.

Your New Optimization Toolkit: A Quick Recap

Think of the strategies we’ve covered as your new optimization toolkit. Each prompt is a specialized instrument designed for a specific task, turning a vague sense of “this is slow” into a targeted, actionable plan.

• Memory Leak Detection: Instead of manually hunting for rogue event listeners, you can now use prompts that ask Claude Code to perform a “component lifecycle audit,” ensuring every resource acquired is properly released. This prevents the slow, insidious performance degradation that plagues long-running applications.
• API and Network Efficiency: We tackled the dreaded N+1 query problem head-on. By prompting for a “database query review,” you shift the burden of identifying sequential database calls to the AI, allowing you to focus on implementing elegant eager-loading solutions that slash network latency.
• Architectural Review: Perhaps most powerfully, we explored how to use prompts for a “concurrency and parallelism analysis.” This moves the conversation from micro-optimizations to macro-performance, identifying opportunities to run tasks in parallel and ensuring your application can handle scale without blocking the main thread.

The Human-AI Partnership: You Are the Architect

It’s crucial to remember that the goal is not to abdicate your responsibility but to amplify your expertise. Claude Code is a tireless analyst with encyclopedic knowledge, but you are the architect with the final say. The AI can suggest a more efficient algorithm or flag a potential memory leak, but it cannot understand the full business context, the specific user experience goals, or the long-term maintainability trade-offs of a project. Your critical thinking is the essential ingredient. You must validate, test, and approve every suggestion, ensuring it aligns with the broader vision for the application. This partnership allows you to spend less time on the “how” of tedious debugging and more time on the “why” of impactful architectural decisions.

Future-Proofing Your Skills: Build Your Prompt Library

The most effective developers in 2025 and beyond won’t be those who resist AI, but those who master the art of directing it. The true long-term value from this process comes from internalizing these patterns and building your own personal library of effective prompts. Don’t just use the templates from this article once. Adapt them. Refine them for your specific tech stack—be it Go, Rust, or a niche framework. When you encounter a new performance bottleneck, document the prompt that solved it. This practice transforms AI-assisted optimization from a one-off trick into a repeatable, powerful part of your standard development workflow. It’s how you turn a novel tool into a lasting competitive advantage, ensuring your skills remain relevant and your applications remain fast for years to come.

Performance Data

Frequently Asked Questions

Q: Why does Claude Code need my package.json for code optimization

It needs to know your frameworks and libraries (like Next.js or Zustand) because optimization strategies vary wildly between them; a generic prompt without this context yields generic, often useless advice

Q: What is the ‘Golden Triangle’ in AI prompting

It is a structure where you define the Goal, provide the Context (full code/architecture), and state the Constraints to ensure the AI gives you a precise, actionable diagnosis rather than a surface-level check

Q: Can Claude Code find memory leaks

Yes, provided you give it enough context, such as event listeners and closure management across multiple files, allowing it to trace data flow and identify improper resource disposal