Code Generation Chain Patterns

Code generation benefits significantly from chaining. Breaking the process into steps improves quality, maintainability, and correctness.

Why Chain Code Generation?

Single-prompt code generation often fails because:

• Complex requirements get lost
• Generated code lacks structure
• Edge cases are forgotten
• Testing is an afterthought
• No validation of correctness

The Code Generation Pipeline

Requirements → Design → Generate → Validate → Test → Refine → Document

Core Patterns

Pattern 1: Requirements to Design

Convert requirements into technical design first:

Pattern 2: Design to Implementation

Generate code from the design:

async function generateFromDesign(design, language) {
  const prompt = `
Generate ${language} code based on this technical design.

DESIGN:
${JSON.stringify(design, null, 2)}

REQUIREMENTS:
1. Follow ${language} best practices
2. Include type annotations
3. Handle all error cases from the design
4. Add inline comments for complex logic
5. Use meaningful variable names

Generate the implementation:
`;

  return await llm.chat({ content: prompt });
}

Pattern 3: Generate with Context

Include relevant existing code:

Multi-Step Code Generation

Step 1: Interface/Contract Definition

async function generateInterface(requirements) {
  const prompt = `
Based on these requirements, define the TypeScript interfaces and function signatures.
Do NOT implement - only define the contracts.

Requirements: ${requirements}

Output format:
- Interface definitions
- Function signatures with JSDoc
- Type definitions
- Error types
`;

  return await llm.chat({ content: prompt });
}

Step 2: Implementation

async function generateImplementation(interfaces) {
  const prompt = `
Implement the following interfaces and function signatures.

INTERFACES AND SIGNATURES:
${interfaces}

IMPLEMENTATION RULES:
1. Implement all functions to match their signatures exactly
2. Throw appropriate errors for invalid inputs
3. Handle edge cases
4. Do NOT modify the interfaces

Generate the implementation:
`;

  return await llm.chat({ content: prompt });
}

Step 3: Validation

async function validateGeneration(interfaces, implementation) {
  const prompt = `
Validate that this implementation correctly implements the interfaces.

INTERFACES:
${interfaces}

IMPLEMENTATION:
${implementation}

Check for:
1. All functions implemented
2. Return types match
3. Parameter types match
4. Error types used correctly
5. No missing functionality

Report any issues found:
`;

  return await llm.chat({ content: prompt });
}

Component Generation Chain

For larger code generation tasks:

Error Handling in Code Generation

Syntax Validation

async function validateSyntax(code, language) {
  // Use language-specific parsers
  try {
    if (language === 'javascript' || language === 'typescript') {
      const ast = parse(code);
      return { valid: true, ast };
    }
    // Add other language parsers
  } catch (error) {
    return {
      valid: false,
      error: error.message,
      line: error.line
    };
  }
}

Logic Validation

async function validateLogic(code, requirements) {
  const prompt = `
Check if this code correctly implements the requirements.

REQUIREMENTS:
${requirements}

CODE:
${code}

Check for:
1. All requirements addressed
2. Edge cases handled
3. No obvious bugs
4. No security vulnerabilities

Report issues as:
{ "issues": [{ "severity": "high|medium|low", "description": "", "line": number }] }
`;

  return await llm.chat({ content: prompt });
}

Exercise: Build a Code Generation Chain

Key Takeaways

• Convert requirements to design before generating code
• Generate interfaces/contracts first, then implementations
• Include existing code context for consistency
• Validate at multiple stages (syntax, logic, requirements)
• Break large generation tasks into components
• Use multi-step chains for complex code

Next, we'll explore test generation chains.