private templates: Map<string, string> = new Map();

registerTemplate(domain: string, template: string): void { this.templates.set(domain, template); }

resolvePrompt(scope: PromptScope, taskSpec: string): string { const base = this.templates.get(scope.domain) ?? this.templates.get('default'); if (!base) throw new Error(No template registered for domain: ${scope.domain});

return base
  .replace('{{CONSTRAINTS}}', JSON.stringify(scope.constraints))
  .replace('{{TASK}}', taskSpec)
  .replace('{{AMBIGUITY}}', scope.ambiguityPolicy);

} }

**Why this architecture:** Separating constraint definition from prompt resolution prevents context drift. The `PromptRouter` ensures every request carries explicit boundaries, eliminating the common failure mode where models silently ignore earlier instructions. The `ambiguityPolicy` field forces the model to either surface unknowns or halt, rather than inventing assumptions.

### Step 2: Task Decomposition & State Tracking

Large objectives fail when fed directly into AI systems. Decompose work into verifiable subtasks with explicit inputs, outputs, and acceptance criteria. Track state to enable rollback and parallel execution.

```typescript
interface Subtask {
  id: string;
  owner: 'ai' | 'human';
  dependencies: string[];
  inputs: Record<string, unknown>;
  acceptanceCriteria: string[];
  status: 'pending' | 'executing' | 'validated' | 'failed';
}

class TaskDecomposer {
  decompose(objective: string, constraints: string[]): Subtask[] {
    return [
      { id: 'design', owner: 'ai', dependencies: [], inputs: { objective, constraints }, acceptanceCriteria: ['architectural_options >= 2', 'data_model_defined'], status: 'pending' },
      { id: 'implementation', owner: 'ai', dependencies: ['design'], inputs: {}, acceptanceCriteria: ['interfaces_match_spec', 'error_handling_present'], status: 'pending' },
      { id: 'testing', owner: 'ai', dependencies: ['implementation'], inputs: {}, acceptanceCriteria: ['coverage >= 0.85', 'property_tests_included'], status: 'pending' },
      { id: 'review', owner: 'human', dependencies: ['testing'], inputs: {}, acceptanceCriteria: ['security_scan_pass', 'performance_baseline_met'], status: 'pending' }
    ];
  }

  validate(subtask: Subtask, output: unknown): boolean {
    return subtask.acceptanceCriteria.every(criterion => {
      // In production, this maps to regex checks, AST analysis, or test runners
      return typeof output === 'object' && output !== null && criterion in (output as Record<string, unknown>);
    });
  }
}

Why this architecture: The TaskDecomposer enforces a plan-execute-validate loop. By modeling subtasks as stateful objects with dependencies, the system prevents premature execution and ensures validation occurs before progression. The owner field explicitly separates AI generation from human oversight, aligning with production safety requirements.

Step 3: Deterministic Verification & CI Integration

AI outputs must pass through the same gates as traditional code. Implement a verification suite that runs static analysis, test execution, and security scanning before marking any subtask as complete.

interface VerificationResult {
  passed: boolean;
  metrics: { coverage: number; lintErrors: number; securityFlags: number };
  report: string;
}

class VerificationRunner {
  async run(subtask: Subtask, artifact: string): Promise<VerificationResult> {
    const coverage = await this.measureCoverage(artifact);
    const lintErrors = await this.runLinter(artifact);
    const securityFlags = await this.scanForVulnerabilities(artifact);

    const passed = coverage >= 0.85 && lintErrors === 0 && securityFlags === 0;

    return {
      passed,
      metrics: { coverage, lintErrors, securityFlags },
      report: passed ? 'Verification passed' : `Failed: coverage=${coverage}, lint=${lintErrors}, security=${securityFlags}`
    };
  }

  private async measureCoverage(code: string): Promise<number> { /* stub */ return 0.92; }
  private async runLinter(code: string): Promise<number> { /* stub */ return 0; }
  private async scanForVulnerabilities(code: string): Promise<number> { /* stub */ return 0; }
}

Why this architecture: Verification is decoupled from generation. The VerificationRunner treats AI output as untrusted input, applying deterministic checks before acceptance. This eliminates the common mistake of trusting AI-generated code based on superficial correctness. The metrics object provides immediate feedback for replanning, and the pass/fail gate integrates directly into CI/CD pipelines.

Pitfall Guide

1. Context Window Drift

Explanation: Long conversations cause models to forget early constraints, leading to scope creep and inconsistent outputs. Fix: Reset context per subtask. Pass constraints explicitly in every prompt. Use versioned prompt templates instead of conversational history.

2. Ambiguity Blindness

Explanation: Models fill missing requirements with plausible defaults, creating hidden technical debt. Fix: Enforce an ambiguityPolicy that requires explicit flagging of unknowns. Block execution until human clarification is provided.

3. Verification Bypass

Explanation: Teams skip deterministic checks because AI output "looks correct," leading to production failures. Fix: Treat all AI artifacts as untrusted. Mandate coverage thresholds, linting, and security scans before merge. Automate gates in CI.

4. Role Contamination

Explanation: Using a single prompt for architecture, implementation, and testing causes the model to mix concerns and degrade output quality. Fix: Route tasks through domain-specific templates. Maintain separate execution contexts for design, coding, and validation phases.

5. Over-Reliance on Chain-of-Thought

Explanation: Reasoning traces are useful for debugging but should not replace test execution or static analysis. Fix: Use reasoning prompts for exploration only. Validate all conclusions with deterministic checks. Never merge based on reasoning alone.

6. Missing Rollback Paths

Explanation: AI-generated refactors or migrations lack safe exit strategies, causing system instability when outputs fail. Fix: Require explicit rollback plans in decomposition. Version prompt configurations and outputs. Implement feature flags for AI-driven changes.

7. Ignoring Cost & Latency Tradeoffs

Explanation: Running complex prompts on high-cost models for trivial tasks wastes budget and slows delivery. Fix: Route tasks by complexity. Use smaller models for boilerplate and scaffolding. Reserve larger models for architecture and security review. Implement prompt caching for repeated patterns.

Production Bundle

Action Checklist

• Define explicit constraints per domain: language, framework, latency, security standards, and ambiguity policy.
• Decompose objectives into 3–10 subtasks with clear owners, dependencies, and acceptance criteria.
• Implement a prompt router that resolves templates based on domain and injects constraints deterministically.
• Integrate verification gates: coverage thresholds, linting, and security scanning before marking subtasks complete.
• Version all prompt templates and outputs. Store them alongside code in the repository for auditability.
• Route tasks by complexity: use lightweight models for scaffolding, heavyweight models for architecture and security.
• Enforce human sign-off on critical logic, security boundaries, and performance baselines.
• Document rollback strategies and feature flag configurations for every AI-assisted deployment.

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Rapid prototyping with clear constraints	Lightweight model + structured prompt template	Fast iteration, low cost, acceptable risk for throwaway code	Low
Core business logic or security boundaries	Heavyweight model + explicit verification suite + human review	High correctness requirement, auditability mandatory	High
Boilerplate generation & scaffolding	Domain-specific template + deterministic validation	Repetitive patterns benefit from automation, low failure risk	Low
Complex refactoring or migration	Decomposed subtasks + rollback plan + CI verification gates	Prevents system instability, ensures traceable changes	Medium
Performance-critical pathways	Human-led design + AI-assisted optimization + benchmark validation	AI lacks runtime context; benchmarks catch regressions	Medium

Configuration Template

{
  "pipeline": {
    "version": "1.0.0",
    "constraints": {
      "language": "typescript",
      "framework": "node",
      "maxLatencyMs": 150,
      "securityStandard": "owasp_top10",
      "coverageThreshold": 0.85
    },
    "ambiguityPolicy": "flag",
    "subtasks": [
      {
        "id": "design",
        "domain": "architecture",
        "template": "design_scope_v1",
        "acceptanceCriteria": ["options >= 2", "data_model_defined", "tradeoffs_documented"]
      },
      {
        "id": "implementation",
        "domain": "implementation",
        "template": "impl_contract_v1",
        "acceptanceCriteria": ["interfaces_match", "error_handling", "input_validation"]
      },
      {
        "id": "testing",
        "domain": "testing",
        "template": "test_suite_v1",
        "acceptanceCriteria": ["coverage >= 0.85", "property_tests", "edge_cases"]
      },
      {
        "id": "review",
        "domain": "security",
        "template": "security_audit_v1",
        "acceptanceCriteria": ["scan_clean", "no_pii_leakage", "performance_baseline"]
      }
    ],
    "verification": {
      "enabled": true,
      "gates": ["coverage", "lint", "security_scan"],
      "failAction": "block_merge"
    }
  }
}

Quick Start Guide

1. Initialize the pipeline configuration: Copy the configuration template into your repository root. Adjust constraints to match your tech stack, latency requirements, and security standards.
2. Register domain templates: Create prompt templates for architecture, implementation, testing, and security review. Inject constraints dynamically using the router pattern. Store templates in a version-controlled directory.
3. Decompose your first objective: Break a feature or refactor into 3–5 subtasks. Assign owners, define dependencies, and write explicit acceptance criteria. Commit the decomposition alongside the configuration.
4. Execute with verification gates: Run the pipeline locally or in CI. Monitor verification results. If any gate fails, the system blocks progression. Review flagged ambiguities and update constraints before retrying.
5. Merge with audit trail: Once all subtasks pass verification, merge the outputs. Ensure prompt versions, configuration snapshots, and verification reports are committed alongside the code for future audits and team onboarding.