AI-Augmented Development Pipelines: Automating Spec-to-Component Workflows

Inputs → Context Enrichment → Component Generation → Quality Validation → Output

interface ProjectContext {
  componentConventions: string    // How components are structured in this project
  designTokens: Record<string, string>  // Available CSS variables/Tailwind tokens
  existingComponents: ComponentSummary[]  // Reusable components to prefer over new ones
  typePatterns: string            // Common TypeScript patterns in the project
  testingPatterns: string         // Testing library conventions
}
 
async function buildProjectContext(projectPath: string): Promise<ProjectContext> {
  // Read 3-5 existing components to extract conventions
  const sampleComponents = await readSampleComponents(projectPath, 5)
 
  const conventions = await openai.chat.completions.create({
    model: 'gpt-4o',
    messages: [
      {
        role: 'system',
        content: 'Analyze these React components and extract the coding conventions used.',
      },
      {
        role: 'user',
        content: `Components:\n${sampleComponents.join('\n\n---\n\n')}
 
Extract:
1. Component structure pattern (named exports, default exports, etc.)
2. Props interface naming convention
3. Event handler naming patterns
4. CSS/styling approach
5. Import organization
6. Error/loading state patterns`,
      },
    ],
    response_format: zodResponseFormat(ConventionsSchema, 'conventions'),
  })
 
  return {
    componentConventions: JSON.stringify(conventions.choices[0].message.parsed),
    designTokens: await readDesignTokens(projectPath),
    existingComponents: await indexExistingComponents(projectPath),
    typePatterns: await extractTypePatterns(projectPath),
    testingPatterns: await readTestPatterns(projectPath),
  }
}

async function interpretSpec(
  spec: ComponentSpec,
  context: ProjectContext
): Promise<InterpretedSpec> {
  const response = await anthropic.messages.create({
    model: 'claude-opus-4-6',
    max_tokens: 2048,
    messages: [
      {
        role: 'user',
        content: `Interpret this component specification for implementation.
 
Spec:
${spec.description}
 
Design notes:
${spec.designNotes}
 
Acceptance criteria:
${spec.acceptanceCriteria.map(c => `- ${c}`).join('\n')}
 
Identify:
1. All props with their types
2. All state variables
3. All user interactions and their handlers
4. Accessibility requirements
5. Any ambiguities that need clarification before implementation`,
      },
    ],
    tools: [interpretedSpecTool],
    tool_choice: { type: 'tool', name: 'record_interpreted_spec' },
  })
 
  const toolUse = response.content.find(b => b.type === 'tool_use')
  return toolUse?.input as InterpretedSpec
}

async function generateComponent(
  interpretedSpec: InterpretedSpec,
  context: ProjectContext
): Promise<GeneratedComponent> {
  const systemPrompt = `You are an expert React/TypeScript engineer. Generate production-quality code that:
- Follows these project conventions: ${context.componentConventions}
- Uses these design tokens where appropriate: ${JSON.stringify(context.designTokens)}
- Prefers these existing components over creating new ones: ${
    context.existingComponents.map(c => c.name).join(', ')
  }
- Uses these TypeScript patterns: ${context.typePatterns}
- Writes tests using: ${context.testingPatterns}`
 
  const response = await openai.chat.completions.create({
    model: 'gpt-4o',
    messages: [
      { role: 'system', content: systemPrompt },
      {
        role: 'user',
        content: `Generate a React component based on this specification:
 
${JSON.stringify(interpretedSpec, null, 2)}
 
Produce:
1. The component file (TypeScript, follow project conventions exactly)
2. A Storybook story covering: default, loading, error, and edge case states
3. An RTL test file covering: render, user interactions, accessibility`,
      },
    ],
    response_format: zodResponseFormat(GeneratedComponentSchema, 'generated_component'),
  })
 
  return response.choices[0].message.parsed!
}

async function validateGenerated(
  generated: GeneratedComponent,
  spec: ComponentSpec,
  interpretedSpec: InterpretedSpec
): Promise<ValidationReport> {
  // Static validation (no LLM needed)
  const typeErrors = await runTypeCheck(generated.component)
  const lintErrors = await runESLint(generated.component)
 
  // Semantic validation
  const semanticCheck = await openai.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [
      {
        role: 'system',
        content: 'Validate that a React component matches its specification.',
      },
      {
        role: 'user',
        content: `Spec requirements: ${JSON.stringify(interpretedSpec)}
 
Generated component:
\`\`\`tsx
${generated.component}
\`\`\`
 
Check:
1. All required props are present with correct types
2. All acceptance criteria are addressed
3. Accessibility attributes are correct
4. Edge cases from the spec are handled`,
      },
    ],
    response_format: zodResponseFormat(ValidationReportSchema, 'validation'),
  })
 
  return {
    typeErrors,
    lintErrors,
    semanticIssues: semanticCheck.choices[0].message.parsed!.issues,
    passesValidation: typeErrors.length === 0 && lintErrors.length === 0,
  }
}