Building AI-Powered Features: A Product Engineering Perspective

traditional_vs_ai:
  traditional:
    behavior: Deterministic
    testing: Input → expected output
    failure: Predictable, catchable
    iteration: Code changes → behavior changes

  ai_powered:
    behavior: Non-deterministic (can vary)
    testing: Input → range of acceptable outputs
    failure: Subtle, quality degradation
    iteration: Prompt/model changes → behavior changes

user_expectations:
  what_users_expect:
    - AI that "just works"
    - Perfect accuracy
    - Instant responses
    - Understanding of context

  reality:
    - AI makes mistakes
    - Quality varies
    - Latency is significant
    - Context is limited

  bridge_the_gap:
    - Set appropriate expectations
    - Design for graceful failure
    - Provide feedback mechanisms
    - Iterate based on real usage

good_ai_use_cases:
  automate_tedious:
    - Summarization
    - Data extraction
    - Categorization
    - Draft generation

  augment_human:
    - Suggestions (human decides)
    - Error detection
    - Information retrieval
    - Translation

  enable_new:
    - Natural language interfaces
    - Semantic search
    - Personalization at scale

  avoid:
    - High-stakes autonomous decisions
    - Where precision is critical
    - Where explanation is required
    - Where AI limitation causes harm

ai_feature_spec:
  standard_sections:
    - Problem statement
    - User stories
    - Requirements

  ai_specific_sections:
    quality_definition:
      - What does "good" look like?
      - Acceptable error rate?
      - Edge cases to handle?

    failure_modes:
      - What happens when AI fails?
      - How do users recover?
      - What's the fallback?

    evaluation:
      - How do we measure success?
      - What metrics matter?
      - How do we get feedback?

    constraints:
      - Latency requirements
      - Cost budget
      - Data privacy considerations

class AISummarizationFeature:
    def __init__(self, llm, validator, metrics):
        self.llm = llm
        self.validator = validator
        self.metrics = metrics

    async def summarize(self, document: str, user_id: str) -> SummaryResult:
        start_time = time.time()

        try:
            # Input validation
            if len(document) > 100000:
                return SummaryResult(
                    success=False,
                    error="Document too long",
                    fallback=document[:500] + "..."
                )

            # Generate summary
            prompt = self._build_prompt(document)
            response = await self.llm.generate(
                prompt,
                max_tokens=300,
                temperature=0.3
            )

            # Validate output
            validation = self.validator.validate(response, document)
            if not validation.passed:
                self.metrics.increment('summarization.validation_failed')
                return SummaryResult(
                    success=False,
                    error="Summary quality check failed",
                    fallback=self._extractive_fallback(document)
                )

            # Success
            self.metrics.timing('summarization.latency', time.time() - start_time)
            self.metrics.increment('summarization.success')

            return SummaryResult(
                success=True,
                summary=response,
                confidence=validation.confidence
            )

        except Exception as e:
            self.metrics.increment('summarization.error')
            return SummaryResult(
                success=False,
                error="Unable to generate summary",
                fallback=self._extractive_fallback(document)
            )

    def _extractive_fallback(self, document: str) -> str:
        """Simple extractive summary as fallback."""
        sentences = document.split('.')
        return '. '.join(sentences[:3]) + '.'

ai_ui_patterns:
  loading_states:
    - Show clear "AI is thinking" indicator
    - Progress indicators for long operations
    - Allow cancellation

  uncertainty_communication:
    - "AI-generated" labels
    - Confidence indicators where appropriate
    - "This may contain errors" disclaimers

  feedback_collection:
    - Thumbs up/down on results
    - "Report an issue" option
    - Easy correction mechanisms

  graceful_degradation:
    - Show fallback when AI fails
    - Offer manual alternative
    - Don't leave user stuck

ai_testing_levels:
  unit_tests:
    - Prompt formatting
    - Input validation
    - Output parsing
    - Business logic around AI

  integration_tests:
    - End-to-end with mocked LLM
    - Error handling paths
    - Timeout behavior

  quality_tests:
    - Golden dataset evaluation
    - Regression detection
    - Edge case coverage

  production_monitoring:
    - User feedback analysis
    - Quality metrics tracking
    - Anomaly detection

class AIFeatureEvaluator:
    def __init__(self, feature, golden_dataset):
        self.feature = feature
        self.golden_dataset = golden_dataset

    def evaluate(self) -> EvaluationReport:
        results = []

        for test_case in self.golden_dataset:
            result = self.feature.process(test_case.input)

            # Compare to expected output
            similarity = self.compare(result.output, test_case.expected)
            passed = similarity >= test_case.threshold

            results.append({
                'test_id': test_case.id,
                'passed': passed,
                'similarity': similarity,
                'expected': test_case.expected,
                'actual': result.output
            })

        return EvaluationReport(
            total=len(results),
            passed=sum(1 for r in results if r['passed']),
            pass_rate=sum(1 for r in results if r['passed']) / len(results),
            results=results
        )

rollout_strategy:
  phase_1_internal:
    - Internal dogfooding
    - Collect feedback
    - Fix major issues

  phase_2_beta:
    - 5% of users
    - Feature flag controlled
    - Monitor closely

  phase_3_gradual:
    - 25% → 50% → 100%
    - Watch metrics at each step
    - Rollback plan ready

  ongoing:
    - Continuous feedback collection
    - Regular quality evaluation
    - Prompt/model improvements

feedback_collection:
  explicit:
    - In-app feedback buttons
    - Support ticket analysis
    - User interviews

  implicit:
    - Feature usage metrics
    - Error/retry rates
    - Time to complete tasks
    - User corrections/edits

  action:
    - Weekly review of feedback
    - Prioritize improvements
    - Update evaluation dataset
    - Iterate on prompts