AI Content Hub

As of April 2026, Claude Code (Anthropic's CLI agent) consistently tops developer rankings for agentic coding tasks. It can spawn subagents, read/write files, run tests, and iterate on complex changes autonomously - making it a true coding partner rather than just a code autocomplete.

Use Claude Code (CLI) for agentic coding tasks — it excels at multi-file refactors and complex changes.

For coding tasks, include relevant file context and error messages to get much better responses.

When asking an LLM to perform a task with a specific format, provide a few examples (2-3) in your prompt. This 'few-shot' approach helps the model understand the desired input-output pattern, leading to more consistent and accurate results.

Challenge: Describe a complex text pattern in plain English and ask an LLM to generate the corresponding regular expression (regex). For example, 'Generate a regex to validate an email address according to RFC 5322.'

Many LLMs are proficient at generating code in various programming languages, debugging, explaining code snippets, and even translating code between languages. However, generated code always requires careful review.

When generating code with an LLM, ask it to explain the solution step-by-step before writing the code. This helps both your understanding and often leads to better quality code as the model thinks through the problem more methodically.

When generating code with an LLM, ask it to explain the solution step-by-step before writing the code. This helps both your understanding and often leads to better quality code as the model thinks through the problem more methodically.

If you frequently use long model names in your prompts or code (e.g., 'gpt-3.5-turbo-instruct-0914'), consider using shorter aliases if your framework allows, or use variables to store them. This can slightly reduce token count in complex prompts.

When asking for code, provide surrounding code snippets or describe the existing architecture. This gives the LLM context, leading to more compatible and accurate code generation.

If there are several ways to implement a feature, ask the LLM to provide a few different code solutions along with their pros and cons. This can help you choose the best approach.

When asking an LLM to generate code, also ask it to include comments explaining the code. This improves readability and maintainability.

Never blindly trust code generated by an LLM. Always thoroughly test it for correctness, security vulnerabilities, and performance issues before using it in production.

When asking for code, specify the programming language and, if relevant, the version (e.g., 'Python 3.9', 'JavaScript ES6'). This helps avoid ambiguity and deprecated features.

When asking an LLM to write code, specifically request that it include error handling (e.g., try-catch blocks, input validation).

When an LLM generates code, ask it to also generate unit tests for that code. This helps ensure correctness and makes future refactoring safer.

If LLM-generated code has errors, provide the error message back to the LLM and ask it to debug and fix the code. Iterate until it works.

LLMs can be surprisingly good at translating code snippets from one programming language to another. Useful for learning new languages or migrating projects.

Provide a working piece of code to an LLM and ask for suggestions on how to optimize it for performance or readability.

LLMs are excellent at generating boilerplate code for common patterns (e.g., setting up a new class, a basic API endpoint, HTML structure).

LLMs can assist in refactoring legacy code by suggesting modernizations, improving readability, or even translating to a new language (with careful review).

Provide code to an LLM and ask it to generate documentation (e.g., docstrings, comments, or a README section) for that code.

Describe the pattern you want to match in natural language, and ask an LLM to generate the corresponding regular expression. Test it thoroughly.

When working with APIs, ask LLMs to generate example code for common integration patterns, including authentication, error handling, and data parsing.

Provide slow database queries to an LLM and ask for optimization suggestions, including index recommendations and query restructuring.

Ask LLMs to review code for potential security vulnerabilities, but always follow up with human security experts for critical applications.

Ask LLMs to generate configuration files for various tools and frameworks based on your requirements and best practices.

Ask LLMs to generate benchmarking code to measure the performance of different algorithms or implementations.

Generate database migration scripts or data transformation code when moving between different systems or updating schemas.

Generate realistic mock data for testing applications, including user profiles, transaction records, and sample content that matches your schema.