Context-Aware Code Generation: Moving Beyond Simple Autocomplete

The Autocomplete Ceiling

Current code generation is impressive but generic:

Prompt: "Write a function to validate email"

Generic output:
function validateEmail(email) {
  const regex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
  return regex.test(email);
}

Fine for tutorials. But in a real codebase:

• Does this match your error handling patterns?
• Does it integrate with your validation library?
• Does it follow your naming conventions?
• Does it return in your standard format?

Generic generation doesn't know. It guesses.

What Context-Aware Generation Looks Like

Same prompt, with context:

Prompt: "Write a function to validate email"

Context provided:
- Existing validation functions in codebase
- Standard response format
- Error handling patterns
- Naming conventions

Context-aware output:
export const validateEmail = (email: string): ValidationResult => {
  if (!email || typeof email !== 'string') {
    return {
      isValid: false,
      error: createValidationError('EMAIL_REQUIRED', 'Email is required'),
    };
  }

  if (!EMAIL_REGEX.test(email)) {
    return {
      isValid: false,
      error: createValidationError('EMAIL_INVALID', 'Invalid email format'),
    };
  }

  return { isValid: true };
};

The output matches existing patterns:

• Uses ValidationResult type
• Uses createValidationError helper
• Returns structured response
• Follows naming convention

This code fits in. Generic code doesn't.

The Context Stack

Context-aware generation requires layers of context:

Generic autocomplete: Layer 1 only.
Context-aware generation: All five layers.

Layer 1: Language/Framework Context

The foundation. Most LLMs have this from training.

What it knows:

• Syntax rules
• Standard library usage
• Common patterns
• Framework idioms

How to enhance:

const languageContext = {
  language: "TypeScript",
  version: "5.0",
  framework: "Next.js 14",
  styleGuide: "Airbnb with modifications",
  preferences: {
    functionStyle: "arrow",
    errorHandling: "Result type over exceptions",
    asyncPattern: "async/await",
  },
};

Layer 2: Codebase Context

Your existing code is the best style guide.

What to capture:

• Similar functions that exist
• Import patterns
• File structure conventions
• Test patterns

How to provide it:

async function getCodebaseContext(task) {
  return {
    // Find similar existing code
    similarFunctions: await findSimilarCode(task, {
      limit: 3,
      minSimilarity: 0.7,
    }),

    // Get import patterns from nearby files
    importPatterns: await getImportPatterns(task.targetFile),

    // Get test patterns if generating function
    testPatterns: await getTestPatterns(task.type),
  };
}

Example output:

{
  similarFunctions: [
    {
      name: "validateUsername",
      source: "// existing function code...",
      location: "src/validators/user.ts"
    }
  ],
  importPatterns: [
    "import { ValidationResult } from '@/types'",
    "import { createValidationError } from '@/utils'"
  ],
  testPatterns: [
    "describe('validator', () => { it('returns valid for good input'..."
  ]
}

Layer 3: Architectural Context

How your system is organized.

What to capture:

• Architecture patterns (layered, hexagonal, etc.)
• Service boundaries
• Data flow patterns
• Error handling strategy

How to structure:

const architectureContext = {
  pattern: "Layered architecture",
  layers: {
    api: "Hono routes in /routes",
    service: "Business logic in /services",
    repository: "Data access in /repositories",
    domain: "Types and entities in /domain",
  },
  conventions: {
    serviceNaming: "{Entity}Service",
    repositoryNaming: "{Entity}Repository",
    errorHandling: "Return Result<T>, don't throw",
    validation: "Validate at API layer, trust below",
  },
  dependencies: {
    validation: "zod",
    database: "drizzle-orm",
    http: "hono",
  },
};

Layer 4: Domain Context

Your business, not just your code.

What to capture:

• Business entities and their relationships
• Validation rules specific to your domain
• Terminology and naming
• Constraints and edge cases

From your knowledge base:

const domainContext = await knowledgeBase.query({
  templates: ["BusinessRules", "Entities", "Constraints"],
  relevant_to: task.description,
});

// Returns:
{
  entities: {
    User: {
      fields: ["id", "email", "status", "tier"],
      rules: ["email must be unique", "status transitions are restricted"]
    }
  },
  businessRules: [
    "Enterprise users require SSO",
    "Email changes require verification",
    "Deleted users are soft-deleted for 30 days"
  ]
}

Layer 5: Current Task Context

What specifically we're building right now.

What to provide:

• The specific requirement
• Why it's needed (context for decisions)
• Constraints for this task
• Related recent decisions

const taskContext = {
  requirement: "Add email validation to registration flow",
  why: "Reduce invalid signups and bounce rate",
  constraints: [
    "Must integrate with existing ValidationResult type",
    "Should handle disposable email domains",
    "Performance: < 10ms for sync validation",
  ],
  relatedDecisions: [
    "We chose not to validate via SMTP due to latency",
    "Disposable email list is in /data/disposable-domains.txt",
  ],
};

Putting It Together

The Generation Pipeline

async function generateContextAwareCode(task) {
  // Gather all context layers
  const context = {
    language: getLanguageContext(),
    codebase: await getCodebaseContext(task),
    architecture: await getArchitectureContext(),
    domain: await getDomainContext(task),
    task: task,
  };

  // Build prompt with context
  const prompt = buildContextualPrompt(task, context);

  // Generate
  const code = await llm.generate(prompt);

  // Validate against conventions
  const validation = await validateAgainstConventions(code, context);

  if (!validation.passes) {
    // Regenerate with feedback
    return generateWithFeedback(task, context, validation.feedback);
  }

  return code;
}

The Contextual Prompt

function buildContextualPrompt(task, context) {
  return `
You are generating code for a ${context.language.framework} project.

## Architecture
${JSON.stringify(context.architecture, null, 2)}

## Similar Existing Code
${context.codebase.similarFunctions.map(f => f.source).join('\n\n')}

## Domain Rules
${context.domain.businessRules.join('\n')}

## Task
${task.requirement}

## Constraints
${task.constraints.join('\n')}

Generate code that:
1. Follows the patterns shown in similar existing code
2. Uses existing utilities and types
3. Respects domain business rules
4. Meets the specific constraints

Return only the code, no explanation.
`;
}

Measuring Improvement

Metric: First-Try Acceptance Rate

What percentage of generated code is accepted without modification?

Metric: Edit Distance

How much do humans change generated code? Lower edit distance = better context awareness.

Metric: Convention Violations

Does generated code pass your linters and conventions? Context-aware generation should have near-zero violations.

Building Your Context Infrastructure

Step 1: Document Your Conventions

Create explicit documentation of:

• Naming patterns
• File organization
• Error handling approach
• Common utilities

Store in your knowledge base, not just README files.

Step 2: Index Your Codebase

Build searchable access to:

• Function signatures and implementations
• Import patterns
• Test patterns
• Type definitions

Tools like codebase indexers or semantic search help here.

Step 3: Capture Domain Knowledge

Structure your business rules:

• Entity definitions
• Validation constraints
• Business logic rules
• Terminology glossary

Step 4: Connect to Generation

Build the pipeline that assembles context for each generation request.

The Competitive Advantage

Generic code generation is available to everyone. Your competitors have it too.

Context-aware generation is unique to your codebase. It:

• Knows your patterns
• Respects your architecture
• Understands your domain
• Fits your conventions

This isn't just faster coding. It's coding that produces consistent, maintainable, correct output.

That's the advantage worth building.