Optimize code efficiency quickly with Claude

Modern development demands efficient code. Whether you’re trimming milliseconds off execution time, refactoring a tangled function, or replacing outdated loops with vectorized operations, Claude helps developers optimize faster and with less friction.

We designed Claude to support the entire efficiency optimization process: 

• Start with Claude.ai: Anthropic’s AI coding tool, available on your web, for exploring lightweight refactors, boilerplate rewriting, or structural suggestions.
• Switch to Claude Code: Anthropic’s agentic coding tool that lives in your terminal, for deep, multi-file reasoning.

Together, Claude.ai and Claude Code make optimizing code faster, safer, and more iterative — from the first hunch to final benchmarking.

Why code efficiency matters — and where Claude fits

Code efficiency touches nearly every layer of software quality: runtime speed, memory usage, deployment cost, scalability, and maintainability. But optimization is often an afterthought, tackled late or inconsistently due to time pressure, tool fatigue, or fear of introducing regressions.

Claude reduces that friction in three key ways:

• Context-rich suggestions: Claude can understand entire files and even full projects, not just isolated snippets.
• Refactor safety: You can request focused changes and get back clearly scoped edits, often with explanations.
• System-level insights: Claude Code can reason across files, helping you weigh tradeoffs and suggest better algorithms or libraries.

Prototyping optimizations with Claude.ai

Claude.ai is the fastest way to get optimization advice with minimal setup. Just paste in a function or script and ask Claude to:

• Identify inefficiencies
• Recommend vectorized alternatives
• Replace libraries with faster equivalents
• Reorder operations for early exit or lazy evaluation
• Reduce nested loops or excessive branching

For example, you can pompt Claude.ai to optimize your Python function using nested lists:

I’m working with this Python function that sums nested lists. Can you make it more efficient?
def sum_nested_lists(lists):
    total = 0
    for sublist in lists:
        for num in sublist:
            total += num
    return total

Claude.ai recommends using itertools.chain to flatten the input lazily and sum() to efficiently accumulate the result. You save a full loop layer and potentially reduce memory usage for large datasets.

import itertools

def sum_nested_lists(lists):
    return sum(itertools.chain.from_iterable(lists))

Going deeper with Claude Code: terminal-level efficiency gains

When optimization gets serious — profiling call stacks, minimizing memory pressure, parallelizing workloads — Claude Code steps in.

Initialize it once in your terminal:

npm install -g @anthropic-ai/claude-code

From there, it becomes your agentic coding assistant that can:

• Analyze project-wide inefficiencies
• Inline optimization suggestions directly into files
• Benchmark refactors against the original logic
• Walk through performance tradeoffs (e.g., memory vs compute)
• Rewrite algorithms for lower complexity

Tool-powered deep dives

Claude Code supports tool-assisted workflows that go beyond text-based reasoning. These tools give you an extra layer of visibility and control when optimizing performance:

Code execution tool: profile functions in a sandbox‍

The code_execution tool lets Claude Code run your function in a secure sandbox and return benchmarks, memory stats, or timing data. This is ideal for verifying the real-world impact of a refactor or comparing algorithm variants before merging. 

Append the tool flag to your prompt: 

-p "Profile this parse_csv() function for memory and runtime bottlenecks" \
       --tools code_execution

Claude runs your function, captures traces (e.g., tracemalloc or timeit), and returns a timing report with annotated suggestions. 

Text editor tool: in-place edits with undo

When Claude identifies inefficiencies, it can make safe, auditable edits across your repo. With the editor tool enabled, you can ask it to replace constructs, rename variables, or rewrite functions — all inline. Changes are grouped into commits and can be reviewed or reverted. Use cases include replacing map with comprehensions, swapping out deprecated APIs, or simplifying multi-line control flows.

These tools allow Claude to operate like a full programming assistant, capable of understanding, editing, and verifying performance improvements in real-time.

Optimizing enormous repos

Large codebases require a strategy to stay token-efficient and focused. Claude Code supports best practices to optimize performance workflows without overwhelming context windows:

• Use a CLAUDE.md: Create a project summary file that explains your directory structure, key performance bottlenecks, and architectural context. Claude will use this for scoped reasoning instead of ingesting everything.
• Focus on subdirectories: Run Claude inside the relevant package (e.g., core/) to avoid reading unrelated files or test data.
• Apply lessons from Velox/GitHub issue #403: Token budgets matter. Provide focused prompts, reduce diff length, and avoid large-file bloat by summarizing intent in advance.

These steps improve Claude’s output quality while minimizing latency and token cost, especially on large enterprise-scale repos.

Example: replacing a recursive DFS with an iterative approach

Prompt Claude Code to find performance bottlenecks in your recursive functions:

Find all recursive functions in the graph_utils module. Are any of them performance bottlenecks, and can you replace them with an iterative version?

Claude Code will: 

• Identify a recursive DFS function
• Analyze stack depth under common input sizes
• Suggest an iterative version using a stack, with equivalent output
• Benchmark the execution time before and after

It may also suggest further tweaks like caching visited nodes with sets or using deque for constant-time pops.

When to use Claude.ai vs Claude Code for performance tuning

Claude.ai and Claude Code each fit into different stages of performance optimization. 

Claude.ai handles quick wins—isolated bottlenecks, small script refactors, and notebook tweaks. It’s ideal for experimenting, prototyping, and learning optimization best practices before diving into the profiler.

Claude Code goes deeper. It vectorizes loops for speed, reduces algorithmic complexity, replaces heavy dependencies, benchmarks end-to-end, and applies multi-file, multi-step changes that cut latency in critical systems like API backends and ML training loops.

In general, start with Claude.ai to fix small, targeted inefficiencies. Switch to Claude Code for legacy code overhauls, call-stack profiling, and enforcing team-wide performance standards. 

Example prompts for optimizing code with Claude

Here’s a curated prompt set you can use to guide Claude toward high-value efficiency refactors:

With Claude.ai (copy into web interface):

• “Can you find any bottlenecks or inefficiencies in this Python script?”
• “Suggest a more efficient sorting or searching approach for this function.”
• “Replace this NumPy operation with a vectorized equivalent.”
• “Which parts of this code could benefit from lazy evaluation?”

With Claude Code (in your terminal):

• “Audit all files for nested loops. Suggest unrolling or replacing with list comprehensions.”
• “Refactor this TensorFlow training loop to minimize memory usage.”
• “Compare the performance of this recursive function to an iterative version.”
• “Can you remove unnecessary allocations in this Rust module and benchmark the result?”

These prompts work best when paired with Claude’s ability to read and write for your project, especially in Claude Code. 

Get started

Claude’s combination of conversational reasoning, codebase awareness, and agentic tools makes it easier to optimize code at every layer.

• Visit Claude.ai and sign up for free. Use it to identify targeted inefficiencies and prototype changes.
• When you're ready to dive into code, install Claude Code and run it in your terminal if you have a Max plan or API access. 

npm install -g @anthropic-ai/claude-code
claude init

If you’re targeting faster execution, reduced memory load, or simpler algorithmic flow, Claude helps you ship cleaner, faster software without wasting cycles on guesswork.