Writing specifications for an AI coding assistant means providing clear, structured, and detailed instructions that the AI can accurately interpret and translate into code. Unlike writing for a human developer, who can infer context and ask clarifying questions, an AI model relies entirely on the information you provide. The more precise the spec, the better the output.

Think of it as the ultimate form of declarative programming. You define the “what” in painstaking detail, so the AI can figure out the “how.” A well-crafted spec acts as both a blueprint and a contract for the AI, minimizing ambiguity and reducing the likelihood of it generating incorrect or irrelevant code. It’s a shift from conversational requests to systematic instruction.

AI coding assistants use large language models (LLMs) that have been trained on vast amounts of text and code. When you provide a specification, the AI breaks it down using natural language processing (NLP) to identify key entities, relationships, and instructions.

The process typically involves a few stages:

1. Parsing the request: The model first tokenizes your input, breaking it down into smaller pieces to analyze grammar and syntax. It identifies keywords, commands, and constraints.
2. Contextual understanding: It then draws upon its training data to understand the technical concepts you’ve mentioned, such as “React component,” “REST API endpoint,” or “asynchronous function.”
3. Identifying patterns: The AI looks for structural patterns in your spec. The use of headings, bullet points, and code blocks helps it differentiate between high-level goals, specific requirements, and concrete examples.
4. Code generation: Based on its interpretation, the model generates code that it predicts is the most probable match for your request. It assembles functions, classes, variables, and logic that fulfill the identified requirements.

Essentially, the AI is performing a high-stakes pattern-matching exercise. A clear, well-formatted spec provides a better pattern to match against, leading to more accurate and useful code.

Writing a detailed spec for an AI might seem like more work upfront, but it pays significant dividends in efficiency and quality. A well-defined spec is crucial for several reasons.

• Reduces ambiguity and errors: Clear instructions leave no room for misinterpretation. This drastically reduces the chances of the AI producing buggy, inefficient, or insecure code.
• Improves code quality and consistency: You can specify coding standards, naming conventions, and architectural patterns directly in the prompt, ensuring the generated code aligns with your project’s standards.
• Increases development speed: While it takes time to write the spec, it saves far more time in debugging and refactoring. A good prompt gets you closer to production-ready code on the first try.
• Enhances predictability: When you provide a consistent and structured format for your specs, the AI’s output becomes more predictable. You can develop a reliable workflow for generating code for similar tasks.

Ultimately, the quality of your input directly determines the quality of the output. Investing in a strong specification is the most effective way to leverage the power of AI coding assistants.

To get the most out of your AI coding assistant, adopt a systematic approach to writing your specifications. The goal is to be an excellent communicator, leaving no detail to chance.

Structure helps the AI differentiate between context, requirements, and examples. Markdown is excellent for this.

• Headings for sections: Use headings like ## Goal, ## Requirements, ## Data Structure, and ## Acceptance Criteria to organize your thoughts.
• Lists for clarity: Use bulleted or numbered lists for discrete requirements. This makes the spec easy for the model to parse.
• Code blocks for examples: Use fenced code blocks for data structures, API responses, or function signatures.

Never assume the AI knows what you mean. State everything, even the obvious.

• Define everything: Specify variable names, function signatures, data types, and library versions.
• Provide context: Briefly explain the purpose of the code. For example, “This function will be used in a React application to fetch user data from a backend API.”
• Set constraints: Clearly state what the code should not do. For example, “Do not use any external libraries for this function” or “The function must not mutate the input array.”

Examples are one of the most powerful tools for guiding an AI. They provide a clear, unambiguous pattern to follow.

• Input/Output examples: For functions, provide sample inputs and their corresponding expected outputs.
• API response examples: If you’re building a feature that interacts with an API, include a JSON snippet of the expected response.
• Data structure examples: Define the shape of objects or data structures you want the AI to work with.

Let’s compare a vague request with a well-structured specification.

Weak Spec:

“Write a function that gets user data.”

This is a gamble. The AI might generate a function that fetches from a fake API, uses outdated syntax, or has a completely different data structure than you need.

Strong Spec:

Goal

Create an asynchronous TypeScript function to fetch user profile data from a REST API endpoint.

Requirements

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• The function should be named getUserProfile.
• It must accept one argument: userId (a string).
• It must use the fetch API to make a GET request.
• The API endpoint is https://api.example.com/users/{userId}.
• The function should handle potential network errors gracefully by throwing an error.
• If the user is not found (404 status), the function should return null.
• It must be strongly typed.

Data Structure

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The expected user profile data structure from the API is:

{
    "id": "string",
    "name": "string",
    "email": "string",
    "isActive": "boolean"
}

Acceptance Criteria

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• Calling getUserProfile('123') with a valid user should return the user profile object.
• Calling getUserProfile('invalid-id') should return null.
• If the network request fails, the function should throw an error.

This detailed spec provides the AI with everything it needs: the function name, arguments, endpoint, error handling logic, and data shape. The resulting code will be far more accurate and immediately useful.

Once your AI coding assistant has helped you build the core features of your application, you’ll need to secure it with robust authentication and user management. This is where a service like Kinde comes in. Instead of writing complex security specs for an AI, you can integrate a dedicated solution designed for it.

Kinde provides powerful, easy-to-install SDKs for popular frameworks like React and Node.js, allowing you to add features like user sign-up, sign-in, and profile management in minutes. Rather than prompting an AI to generate authentication logic from scratch—which can be risky—you can use Kinde’s secure and battle-tested infrastructure. This frees you up to focus on prompting the AI for your application’s unique features, while leaving the critical security components to the experts.

• React SDK
• Node.js SDK
• Top questions about Kinde authentication
• View and edit user properties