AI and Technical Debt: New Challenges

ai_debt_categories:
  model_debt:
    - Outdated models still in use
    - Deprecated API versions
    - Untracked model dependencies
    - Missing model versioning

  data_debt:
    - Training data quality issues
    - Undocumented data pipelines
    - Data drift unmonitored
    - Missing data validation

  prompt_debt:
    - Unversioned prompts
    - Untested prompt changes
    - Duplicated prompt logic
    - Hardcoded prompts

  evaluation_debt:
    - No quality baselines
    - Missing regression tests
    - Inadequate monitoring
    - No golden datasets

  infrastructure_debt:
    - Manual deployments
    - No A/B testing capability
    - Missing fallbacks
    - Cost untracked

warning_signs:
  model_staleness:
    symptom: "We've been using the same model since launch"
    risk: Performance degradation, missing improvements

  prompt_chaos:
    symptom: "Each engineer has their own version of the prompt"
    risk: Inconsistent behavior, hard to improve

  evaluation_void:
    symptom: "We think it works, but we haven't measured"
    risk: Silent quality degradation

  data_mystery:
    symptom: "We're not sure what data the model was trained on"
    risk: Unexplainable failures, bias issues

  dependency_ignorance:
    symptom: "We just call the API, don't track versions"
    risk: Breaking changes surprise you

class AIDebtAssessment:
    def assess(self, system: AISystem) -> DebtReport:
        scores = {}

        # Model debt
        scores['model'] = self._assess_model_debt(system)

        # Prompt debt
        scores['prompt'] = self._assess_prompt_debt(system)

        # Data debt
        scores['data'] = self._assess_data_debt(system)

        # Evaluation debt
        scores['evaluation'] = self._assess_evaluation_debt(system)

        # Infrastructure debt
        scores['infrastructure'] = self._assess_infra_debt(system)

        return DebtReport(scores=scores, recommendations=self._recommend(scores))

    def _assess_prompt_debt(self, system) -> float:
        factors = [
            system.prompts_versioned,
            system.prompts_tested,
            system.prompts_centralized,
            system.prompt_changes_tracked,
        ]
        return sum(factors) / len(factors)

model_lifecycle:
  version_tracking:
    - Track which model version in use
    - Document when deployed
    - Record performance at deployment

  update_cadence:
    - Regular evaluation against new models
    - Scheduled review (quarterly)
    - Clear upgrade criteria

  deprecation_handling:
    - Monitor for deprecation notices
    - Plan migration before deadline
    - Test alternatives proactively

class ModelRegistry:
    def __init__(self):
        self.models = {}

    def register(self, name: str, model_info: dict):
        self.models[name] = {
            **model_info,
            'registered_at': datetime.now(),
            'last_evaluated': None,
            'deprecated': False
        }

    def check_for_updates(self):
        """Check if newer model versions are available."""
        alerts = []
        for name, info in self.models.items():
            latest = get_latest_model_version(info['provider'], info['family'])
            if latest != info['version']:
                alerts.append({
                    'model': name,
                    'current': info['version'],
                    'latest': latest
                })
        return alerts

prompt_governance:
  centralization:
    - Single source of truth for prompts
    - Named, versioned prompts
    - Shared library/registry

  versioning:
    - Semantic versioning for prompts
    - Change documentation
    - Rollback capability

  testing:
    - Tests for each prompt
    - Golden dataset evaluation
    - Regression detection

  review_process:
    - PR review for prompt changes
    - A/B testing for significant changes
    - Impact assessment

# Prompt registry pattern
class PromptRegistry:
    def __init__(self):
        self.prompts = {}
        self.versions = {}

    def register(self, name: str, template: str, version: str):
        if name not in self.prompts:
            self.prompts[name] = {}
            self.versions[name] = []

        self.prompts[name][version] = template
        self.versions[name].append(version)

    def get(self, name: str, version: str = None) -> str:
        if version is None:
            version = self.versions[name][-1]  # Latest
        return self.prompts[name][version]

    def diff(self, name: str, v1: str, v2: str) -> str:
        """Show difference between prompt versions."""
        import difflib
        p1 = self.prompts[name][v1]
        p2 = self.prompts[name][v2]
        return '\n'.join(difflib.unified_diff(
            p1.splitlines(), p2.splitlines(),
            fromfile=f'{name}@{v1}', tofile=f'{name}@{v2}'
        ))

# Usage
prompts = PromptRegistry()
prompts.register(
    'summarize',
    'Summarize the following text in 3 bullet points:\n{text}',
    version='1.0.0'
)

evaluation_infrastructure:
  golden_datasets:
    - Curated test cases
    - Regular updates
    - Coverage analysis

  automated_evaluation:
    - CI/CD integration
    - Regression detection
    - Quality gates

  monitoring:
    - Production quality tracking
    - User feedback collection
    - Anomaly detection

class EvaluationPipeline:
    def __init__(self, golden_dataset, metrics):
        self.golden_dataset = golden_dataset
        self.metrics = metrics
        self.baseline = None

    def run_evaluation(self, model, prompt_version) -> dict:
        results = []

        for case in self.golden_dataset:
            output = model.generate(case.input)
            metrics = {
                metric.name: metric.evaluate(output, case.expected)
                for metric in self.metrics
            }
            results.append(metrics)

        aggregate = self._aggregate(results)

        # Check for regression
        if self.baseline:
            regression = self._check_regression(aggregate, self.baseline)
            if regression:
                raise RegressionDetected(regression)

        return aggregate

    def update_baseline(self, results: dict):
        self.baseline = results

debt_prioritization:
  critical:
    - Security vulnerabilities
    - Data privacy issues
    - Production failures

  high:
    - Quality regression
    - Cost inefficiency
    - Missing monitoring

  medium:
    - Outdated models
    - Untested prompts
    - Missing documentation

  low:
    - Code cleanup
    - Minor optimizations
    - Nice-to-have tooling

incremental_approach:
  each_sprint:
    - 10-20% time for debt reduction
    - Focus on one category
    - Measurable improvement

  quarterly:
    - Debt assessment
    - Prioritization review
    - Major initiatives

  ongoing:
    - Don't add new debt
    - Improve what you touch
    - Document decisions