mentation pattern using TypeScript.

Step 1: Context Assembly Architecture

AI models degrade in accuracy when context is fragmented. The review system must bundle three distinct layers:

1. Intent & Scope: What the change is supposed to achieve
2. Constraints: Security, performance, backward compatibility, and deployment rules
3. Diff Payload: The complete patch or file set under review

interface ReviewContext {
  objective: string;
  domain: 'frontend' | 'backend' | 'data-pipeline' | 'infrastructure';
  constraints: string[];
  affectedFiles: string[];
  diffPayload: string;
}

class ReviewContextBuilder {
  private context: ReviewContext;

  constructor() {
    this.context = {
      objective: '',
      domain: 'backend',
      constraints: [],
      affectedFiles: [],
      diffPayload: ''
    };
  }

  setObjective(goal: string): this {
    this.context.objective = goal;
    return this;
  }

  addConstraint(rule: string): this {
    this.context.constraints.push(rule);
    return this;
  }

  setDiff(patch: string): this {
    this.context.diffPayload = patch;
    return this;
  }

  build(): ReviewContext {
    if (!this.context.objective || !this.context.diffPayload) {
      throw new Error('Review context requires objective and diff payload');
    }
    return { ...this.context };
  }
}

Step 2: Prompt Template Engineering

The prompt must enforce categorical evaluation and suppress conversational filler. It should explicitly forbid assumption generation and require evidence-backed findings.

const REVIEW_TEMPLATE = `
You are a senior systems engineer conducting a pre-merge risk assessment.
Your mandate is to identify failure modes, not validate implementation choices.

Context:
- Objective: {{objective}}
- Domain: {{domain}}
- Constraints: {{constraints}}
- Affected Files: {{affectedFiles}}

Diff Payload:
{{diffPayload}}

Evaluation Categories:
1. Scope Alignment: Does the change solve the stated objective without introducing unrelated modifications?
2. Correctness Boundaries: Are null, empty, malformed, and boundary inputs handled? Are error states contained?
3. Security Posture: Are authentication boundaries preserved? Is user input validated? Are secrets or tokens exposed in logs or responses?
4. Failure Resilience: How does the system behave under dependency timeout, partial failure, or network degradation? Does it fail open or closed?
5. Performance Topology: Are there unbounded loops, N+1 query patterns, or hot-path latency additions?
6. Test Validity: Do tests verify intent or just implementation? Are negative and failure paths covered?
7. Maintainability: Is duplication introduced? Are naming conventions consistent with the existing codebase?
8. Deployment Topology: Does this require migrations, feature flags, or config updates? Is rollback safe?

Output Requirements:
- Verdict: SAFE | REQUIRES_CHANGES | HIGH_RISK
- Risk Register: List 3-7 risks. Each must include: category, severity, code evidence, and mitigation.
- Test Gaps: Identify missing validation scenarios.
- Clarification Queue: Questions that must be resolved before merge.
- Minimal Alternative: If scope is excessive, propose a constrained implementation.

Rules:
- Reference specific functions, lines, or modules.
- Do not invent code or assume missing context.
- If uncertain, state what evidence would resolve the ambiguity.
`;

Step 3: Response Parsing & Validation

AI outputs are unstructured by default. Production systems should enforce schema validation to extract actionable data.

interface ReviewOutput {
  verdict: 'SAFE' | 'REQUIRES_CHANGES' | 'HIGH_RISK';
  riskRegister: Array<{
    category: string;
    severity: 'LOW' | 'MEDIUM' | 'HIGH' | 'CRITICAL';
    evidence: string;
    mitigation: string;
  }>;
  testGaps: string[];
  clarificationQueue: string[];
  minimalAlternative?: string;
}

function parseReviewResponse(raw: string): ReviewOutput {
  // In production, use a JSON schema validator or structured output API
  // This is a simplified extraction pattern
  const verdictMatch = raw.match(/Verdict:\s*(SAFE|REQUIRES_CHANGES|HIGH_RISK)/i);
  if (!verdictMatch) throw new Error('Invalid review output: missing verdict');

  return {
    verdict: verdictMatch[1] as ReviewOutput['verdict'],
    riskRegister: extractRisks(raw),
    testGaps: extractList(raw, 'Test Gaps:'),
    clarificationQueue: extractList(raw, 'Clarification Queue:'),
    minimalAlternative: extractBlock(raw, 'Minimal Alternative:')
  };
}

Architecture Decisions & Rationale

• Separation of Context and Diff: LLMs perform significantly better when constraints are isolated from the code payload. Mixing them causes attention dilution.
• Explicit Output Schema: Forcing categorical responses prevents the model from defaulting to conversational summaries. It also enables programmatic integration with CI/CD pipelines.
• Evidence Requirement: Mandating code references prevents hallucination and forces the model to ground its analysis in the actual diff.
• Verdict Tiers: SAFE, REQUIRES_CHANGES, and HIGH_RISK provide clear merge gates. This aligns with standard engineering risk matrices and simplifies reviewer decision-making.

Pitfall Guide

1. The Validation Trap

Explanation: Asking Is this code good? or Does this look okay? triggers the model's alignment training toward reassurance. The output becomes a checklist of compliments with minor style suggestions. Fix: Replace approval-seeking prompts with risk-seeking directives. Use What failure modes exist in this change? or Identify the highest-risk assumptions before merge.

2. Context Fragmentation

Explanation: Pasting isolated functions or truncated diffs forces the model to guess surrounding dependencies, type definitions, and project conventions. This dramatically increases false positives and missed risks. Fix: Always provide the complete patch, relevant type definitions, and configuration files. If the diff exceeds context limits, split the review by module and aggregate findings.

3. Constraint Blindness

Explanation: Omitting business rules, security policies, or performance SLAs causes the model to evaluate code against generic best practices rather than your actual production requirements. Fix: Inject explicit constraints into the context payload. Examples: Tokens must expire within 15 minutes, No direct database writes in the request path, Must support idempotent retries.

4. Test Illusion

Explanation: AI-generated tests often validate the exact path the model used to generate the code. They confirm the happy path but ignore error boundaries, malformed inputs, or concurrent execution states. Fix: Require the review prompt to explicitly audit test intent vs. implementation coupling. Mandate negative tests, timeout simulations, and boundary condition coverage.

5. Rollback Neglect

Explanation: Developers focus on forward deployment and ignore backward compatibility, migration steps, or feature flag requirements. AI models rarely infer deployment topology unless explicitly prompted. Fix: Include a deployment topology category in every review. Require answers to: Can this be reverted without data loss?, Does it require config changes?, What monitoring signals indicate failure?

6. Prompt Drift

Explanation: Modifying the checklist structure mid-review or adding ad-hoc questions breaks the evaluation consistency. The model loses its categorical anchor and reverts to general commentary. Fix: Version control your review templates. Treat prompts as infrastructure code. Use configuration files or environment variables to inject constraints rather than rewriting the prompt manually.

7. Over-Reliance on AI Verdicts

Explanation: Treating the AI output as authoritative rather than advisory. The model lacks visibility into incident history, undocumented business rules, traffic patterns, and internal security audits. Fix: Use AI findings as a pre-filter. Human reviewers must validate severity ratings, dismiss false positives, and approve mitigations. The AI surfaces risks; engineers own the decision.

Production Bundle

Action Checklist

• Define review objective and domain before generating the diff
• Inject explicit constraints (security, performance, backward compatibility)
• Provide complete patch context, not isolated functions
• Run the structured checklist prompt against the diff
• Validate AI output against the required schema
• Address HIGH_RISK findings before requesting human review
• Generate PR description using the review findings
• Archive the review output in the pull request for auditability

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Minor UI tweak or copy update	Ad-hoc review + linter	Low risk, minimal system impact	Negligible
New API endpoint or data model	Structured checklist protocol	Requires security, validation, and rollback assessment	Low (saves 30-40 min human review)
Authentication or payment flow	Structured checklist + manual security audit	High compliance risk, requires domain expertise	Medium (justified by incident prevention)
Infrastructure or deployment change	Structured checklist + rollback simulation	Failure modes are catastrophic, require topology validation	High (prevents outages)
Legacy code refactor	Structured checklist + regression test suite	High risk of silent behavioral changes	Medium (reduces regression bugs)

Configuration Template

# .ai-review-config.yaml
review:
  template_version: "2.1"
  output_format: "structured"
  required_categories:
    - scope_alignment
    - correctness_boundaries
    - security_posture
    - failure_resilience
    - performance_topology
    - test_validity
    - maintainability
    - deployment_topology
  constraints:
    - "No direct database writes in request handlers"
    - "All user inputs must be validated against schema"
    - "Secrets must never appear in logs or error responses"
    - "Changes must be backward compatible unless flagged"
  verdict_thresholds:
    SAFE: 0 critical, 0 high
    REQUIRES_CHANGES: 0 critical, <=2 high
    HIGH_RISK: >=1 critical OR >=3 high
  ci_integration:
    block_merge_on: "HIGH_RISK"
    require_human_approval_for: "REQUIRES_CHANGES"

Quick Start Guide

1. Install a prompt versioning tool: Store your review template in a shared configuration file or repository. Never hardcode prompts in chat interfaces.
2. Configure your diff extractor: Use git diff --unified=10 or your IDE's patch export to capture complete context. Ensure type definitions and config files are included if they change.
3. Inject constraints: Add your team's security, performance, and deployment rules to the context payload. Treat these as non-negotiable evaluation criteria.
4. Run the review: Execute the structured prompt against the diff. Parse the output using the required schema. Address any HIGH_RISK findings before opening the PR.
5. Attach findings: Paste the AI review summary into the pull request description. This gives human reviewers a pre-filtered risk register and reduces merge latency.

By treating AI-assisted development as a two-phase workflow—generation followed by structured risk assessment—teams retain velocity while enforcing production-grade engineering standards. The checklist is not a replacement for human judgment; it is a force multiplier that surfaces failure modes before they reach the merge queue.