This chapter is valuable whether or not the reader is a developer. Developers will find that Claude dramatically accelerates their workflow — from first draft to optimised, documented, production-ready code. Non-developers can use Claude to write scripts, automate repetitive tasks, understand code they receive, and build simple tools — without needing to learn programming from scratch.
Quick Answer
Writing code with Claude is built on one principle: knowing how to describe what you want clearly — not knowing how to write the code itself. The same specificity skills that improve any Claude interaction produce better code: specify the language and version, describe inputs and outputs with examples, list requirements and edge cases, and indicate coding conventions. Code generation, debugging, code review, documentation, and task automation all follow structured prompt patterns covered in this chapter.
Why Is Claude Exceptionally Good at Code?
For Developers
First draft to production-ready code dramatically faster
Debugging conversations that diagnose in minutes, not hours
Code reviews that catch security and performance issues
Documentation written alongside the code
Targeted optimisation with explained improvements
For Non-Developers
Automation scripts for repetitive file or data tasks
Data cleanup tools beyond spreadsheet formula limits
Google Apps Scripts for Sheets and Gmail workflows
Understanding code received from engineers
Simple web scrapers for research tasks
Code has properties that make Claude particularly effective. It is unambiguous — either it works or it does not. It has established patterns — Claude has processed millions of examples across languages and frameworks. It is verifiable — code can be run immediately and provides direct feedback. And errors are explicit — error messages say exactly what is wrong and where, making debugging conversations highly productive.
Part 1
Code Generation
How Do You Prompt Claude to Generate Clean, Working Code?
The most common mistake in coding prompts is being too abstract. Compare:
❌ Too abstract:
Write a function to process user data.
✅ Specific and useful:
Write a Python function that:
- Takes a list of user dictionaries as input
(each with keys: name, email, signup_date, plan)
- Filters to users who signed up in the last 30 days
- Returns a list sorted by signup_date (newest first)
- Handles missing keys gracefully (default to None)
- Includes type hints
Example input:
[
{"name": "Alice", "email": "alice@example.com",
"signup_date": "2024-03-15", "plan": "pro"},
{"name": "Bob", "email": "bob@example.com",
"signup_date": "2024-01-10", "plan": "free"}
]
Expected output: List of user dicts from last 30 days, sorted newest first.
The Code Generation Template
Write [LANGUAGE] code that:
PURPOSE:
[What this code needs to accomplish]
INPUTS:
[What data/parameters it receives, with types and examples]
OUTPUTS:
[What it should return or produce, with example]
REQUIREMENTS:
- [Specific requirement 1]
- [Specific requirement 2]
CONSTRAINTS:
- [Language version, library restrictions, performance needs]
- [Style conventions: type hints, docstrings, etc.]
EDGE CASES TO HANDLE:
- [Edge case 1]
- [Edge case 2]
Please include:
- Comments explaining non-obvious logic
- Error handling where appropriate
- A brief usage example at the end
Language-Specific Examples
Python
Data Processing Script
Write a Python script that:
PURPOSE:
Read a CSV of sales transactions, clean the data,
and generate a summary report.
INPUT FILE FORMAT:
CSV with columns: date, product_id, quantity, unit_price,
customer_id, region
DATA ISSUES TO HANDLE:
- Some rows have missing quantity (skip those rows)
- Date format is inconsistent (YYYY-MM-DD and MM/DD/YYYY)
- Some unit_price values are strings with $ sign
OUTPUT:
1. A cleaned CSV (same columns, consistent formatting)
2. A printed summary: total revenue by region, top 5 products,
month-over-month trend, rows cleaned vs skipped
Use pandas. Add logging. Python 3.10+, include type hints.
JavaScript
API Integration
Write a JavaScript function that:
PURPOSE:
Fetch user data from our API and display it in a formatted table.
API DETAILS:
- Endpoint: GET https://api.example.com/users
- Auth: Bearer token in Authorization header
- Response: { users: [{id, name, email, role, lastLogin}] }
REQUIREMENTS:
- Async/await pattern
- Handle loading / error / empty states
- Sort users by lastLogin (most recent first)
- Format lastLogin as "X days ago"
DOM TARGETS: #user-table-body, #loading-spinner, #error-message
Vanilla JS only. No external libraries.
SQL
Complex Query
Write a PostgreSQL query to find high-value customers at churn risk.
TABLES:
- orders (id, customer_id, created_at, status, total_amount)
- customers (id, name, email, signup_date, plan_type)
DEFINITIONS:
- High value: Total spend > $5,000 lifetime
- Churn risk: No order in last 90 days AND had 2+ orders
in the 90 days before that
OUTPUT: customer_id, name, email, lifetime_spend,
last_order_date, previous_order_count, days_since_last_order
Sort by lifetime_spend descending. Comment each CTE.
Output Checklist
Always request: complete runnable code with no placeholders, required imports at the top, a usage example showing how to call it, any environment variables or configuration needed, and notes on any assumptions made.
Part 2
Debugging
How Do You Debug Code Effectively with Claude?
Debugging is where Claude often provides the most dramatic time savings. A bug that might take an hour to track down can be diagnosed in minutes when the right information is provided. Always supply the complete debugging package — not just the error message.
LANGUAGE & VERSION: [e.g., Python 3.11, Node.js 18]
WHAT I'M TRYING TO DO:
[Brief description of intended behavior]
THE CODE:
[Paste the relevant function or module]
THE ERROR MESSAGE:
[Paste the FULL error message including stack trace]
WHAT I'VE ALREADY TRIED:
[List what has been attempted so far]
WHEN IT HAPPENS:
[Always? Only with certain inputs? Specific conditions?]
EXAMPLE INPUT THAT TRIGGERS THE BUG:
[The exact input that causes the error]
Three Debugging Patterns
Pattern 1
Error Message Diagnosis
Getting this error in [language]:
[Paste full error with stack trace]
Relevant code:
[Paste code]
What is causing this and how to fix it?
Pattern 2
Logic Bug — No Error, Wrong Output
Code runs without errors but produces wrong output.
Expected behavior: [What it should do]
Actual behavior: [What it actually does]
Code: [Paste code]
Test case:
Input: [example] | Expected: [expected] | Actual: [actual]
Walk through what the code actually does step by step,
then identify where the logic goes wrong.
Pattern 3
Performance Issue
Code works correctly but is too slow.
Currently: [X seconds] on [dataset size]. Target: under [target].
Code: [Paste code]
Please identify bottlenecks, suggest specific optimizations,
estimate improvement per optimization, and show optimized version.
Part 3
Code Review & Optimization
How Do You Run a Comprehensive Code Review with Claude?
Please review this [language] code for:
CORRECTNESS: Logic errors, unhandled edge cases, potential runtime errors
SECURITY: SQL injection, auth issues, sensitive data exposure, input validation
PERFORMANCE: N+1 queries, unnecessary loops, memory concerns, caching
CODE QUALITY: Naming clarity, best practices, DRY violations, bloated functions
Prioritise issues by severity and provide the fixed version.
[Paste code]
🔴
Critical — fix before shipping
Security vulnerabilities, data loss risks, logic errors producing wrong results
Optimize this code specifically for [performance / readability /
memory usage / maintainability].
Current code: [Paste code]
Constraints:
- Must maintain the same public interface
- Python 3.10+ only, standard library, no new dependencies
Show: optimized version, what changed and why, any trade-offs.
Part 4
Documentation
How Do You Generate Code Documentation with Claude?
Generating Docstrings
Write comprehensive docstrings for all functions in this code.
Follow [Google / NumPy / Sphinx] format.
For each function include: one-line summary, extended description
(if complex), Args with types and descriptions, Returns type and
description, Raises for any exceptions, and at least one Example.
[Paste code]
Generating README Documentation
Write a README.md for this project.
PROJECT CONTEXT: [What it does and who uses it]
Include: title and one-paragraph description, features list,
prerequisites, step-by-step installation, 3+ usage examples,
configuration options, contributing guidelines, license section.
Tone: Clear and welcoming. Assume developer unfamiliar with this
codebase.
[Paste code or describe the project]
Explaining Complex Code
Explain this code as if teaching a junior developer who knows
[language] basics but hasn't seen this pattern.
Walk through: high-level summary (2-3 sentences), each major section
and why it's structured that way, non-obvious design decisions and
their reasoning, what someone would need to change to modify behavior.
[Paste code]
Part 5
For Non-Developers
How Can Non-Developers Use Claude for Practical Coding Tasks?
Four use cases are immediately practical for anyone — regardless of coding background:
Use Case 1
Google Sheets Automation
Write a Google Apps Script that runs every Monday and sends
an email summary of a Google Sheet.
Columns: A = Task name, B = Owner, C = Due date,
D = Status (Not Started / In Progress / Complete)
Email should include: tasks due this week (sorted by due date),
overdue tasks (highlighted), % complete across all tasks.
Sheet name: "Project Tracker"
Use Case 2
File Organisation Script
Write a Python script that organises files in Downloads folder.
Rules:
- PDFs → ~/Documents/PDFs
- Images (jpg, png, gif, webp) → ~/Pictures/Downloads
- Videos (mp4, mov, avi) → ~/Movies/Downloads
- Zip files → ~/Documents/Archives
- Everything else → leave in place
Safety: never overwrite (add _1, _2 suffix), print summary
before moving, ask for confirmation, log to organize_log.txt
Use Case 3
CSV Data Cleanup
Write a Python script that cleans a CSV exported from [tool].
Issues to fix:
- Phone numbers inconsistent → standardise to +1-555-123-4567
- Invalid email addresses → remove those rows
- Dates in MM/DD/YYYY → convert to YYYY-MM-DD
- Company names inconsistent → standardise to Title Case
Input: filename as command line argument
Output: [original_name]_cleaned.csv
Print: rows processed, rows removed, changes made
Use Case 4
Web Scraping for Research
Write a Python script using BeautifulSoup to extract job listings
from [website URL] (only where scraping is permitted).
Extract per listing: job title, company, location, date posted,
salary range (if available), job URL.
Output: jobs.csv. Add 2-second delays, handle pagination,
skip listings without a title, print progress.
What Is Claude Code and When Does It Make Sense?
For developers who want to go significantly deeper, Anthropic offers Claude Code — a command-line tool that brings Claude directly into the development environment. Unlike the web interface where code must be pasted in manually, Claude Code can read an entire project, make file edits with approval, run shell commands, maintain context across files, and see actual test failures to fix them in the codebase.
Claude Code makes sense for developers working on substantial projects who want AI assistance at the project level rather than at the snippet level, and who are comfortable with terminal tools. The web interface approach covered in this chapter handles the vast majority of coding tasks. Visit Anthropic's documentation for current installation instructions as details evolve.
What Are the Most Common Coding Mistakes When Using Claude?
Mistake 1: Accepting Code Without Testing
❌ Copy code directly into production
✅ Always test with actual data and environment before deploying
Mistake 2: Pasting Only the Error, Not the Code
❌ "I'm getting a TypeError — what's wrong?"
✅ Paste the full debugging package: code + error + context
Mistake 3: Not Specifying the Environment
❌ "Write a Python script for this"
✅ "Python 3.11. Dependencies allowed: pandas, requests.
Must run on Windows and macOS."
Mistake 4: Too Many Requirements at Once
❌ 20 requirements in one prompt for a complex system
✅ Build incrementally: core first, then features one at a time
Mistake 5: Not Asking for Explanation
❌ Copy code you don't understand
✅ "After writing the code, explain the key design decisions
and anything non-obvious."
Understanding the code matters — especially when something breaks in production.
Key Takeaways
Specificity is everything — Input format, output format, edge cases, and constraints produce far better code than abstract requests
The debugging package works — Language, code, full error, what was tried, example input; no guessing required
Code review finds what is missed — Prioritise security and correctness over style
Documentation is painless — Generate it alongside the code; no excuse for undocumented work
Non-developers can use this too — Automation, data cleanup, and file scripts are all accessible with clear descriptions
Test everything — Claude's code is usually correct; the local environment may not match
Build incrementally — Core functionality first, features second, optimisation third
Assignment
For developers: Take a piece of real code from a current project. Run the comprehensive code review prompt. Implement at least one Critical and one Important finding.
For non-developers: Identify one repetitive manual task — file organising, data formatting, copying between tools. Use Claude to build a script that automates it.
For everyone: Find a piece of code that is not fully understood. Ask Claude to explain it in plain language. Then ask what would need to change to modify its behaviour.
Frequently Asked Questions
No. The key skill is knowing how to describe what you want clearly — not knowing how to write the code itself. Non-developers can use Claude to write automation scripts, clean up data files, organise folders, and build simple tools without learning programming from scratch. The prompting skills covered throughout the Claude Mastery series — being specific, providing context, showing examples — are the same skills that produce good code from Claude.
Provide a complete debugging package: the language and version, a description of intended behaviour, the full code for the relevant function or module, the complete error message including the stack trace, what has already been tried, whether the error always occurs or only under specific conditions, and an example input that triggers the bug. This gives Claude everything needed to diagnose accurately rather than guess. Pasting only the error message without the code and context is the most common debugging mistake.
A comprehensive Claude code review should cover four areas: correctness (logic errors, unhandled edge cases, potential runtime errors), security (SQL injection risks, authentication issues, sensitive data exposure, input validation gaps), performance (inefficient operations, memory concerns, caching opportunities), and code quality (naming clarity, best practices, DRY violations, functions doing too much). Ask Claude to prioritise findings by severity — critical issues to fix before shipping, important issues to address soon, and minor improvements.
Claude Code is a command-line tool from Anthropic that integrates Claude directly into a development environment. Unlike the web interface where code is pasted in manually, Claude Code can read an entire project, make file edits directly, run shell commands, maintain context across multiple files, and see real test failures to fix them in the codebase. It makes sense for developers working on substantial projects who want AI assistance at the project level rather than snippet level. The web interface approach covers the vast majority of coding tasks.
Specificity is everything. The most common mistake is being too abstract. Effective code prompts specify the programming language and version, the exact input format with types and examples, the expected output with examples, requirements and constraints, edge cases to handle, and coding conventions such as type hints or docstrings. The more precisely the expected behaviour is described, the less revision the resulting code requires. Build incrementally — core functionality first, then features, then optimisation.