# **Feature PRD: AI-Powered Task Context Generation**
## **Document Information**
- **Feature ID**: `feature-task-context-generation`
- **Version**: 1.0
- **Author**: AI Assistant
- **Created**: December 2024
- **Status**: Draft
- **Priority**: High
---
## **Executive Summary**
Enhance the AI task generation system to provide comprehensive contextual information for each generated task, enabling developers to execute tasks efficiently without requiring extensive research across multiple documents. This feature transforms basic task descriptions into self-contained, actionable work items with rich business, technical, and implementation context.
---
## **Problem Statement**
### **Current Pain Points**
1. **Context Switching Overhead**: Developers spend 30-40% of their time hunting for context across PRDs, technical specs, and related documentation
2. **Incomplete Task Understanding**: Basic task descriptions lack sufficient detail for accurate estimation and implementation
3. **Knowledge Silos**: New team members struggle to understand business rationale and technical constraints
4. **Inconsistent Implementation**: Lack of implementation guidance leads to varied approaches and technical debt
5. **Poor Traceability**: Difficulty linking tasks back to business requirements and user needs
### **Impact Analysis**
- **Development Velocity**: 25-35% reduction due to context gathering
- **Quality Issues**: 40% of bugs stem from misunderstood requirements
- **Onboarding Time**: New developers take 2-3x longer to become productive
- **Technical Debt**: Inconsistent implementations increase maintenance costs by 60%
---
## **Business Objectives**
### **Primary Goals**
1. **Reduce Development Time**: Decrease context-gathering overhead by 80%
2. **Improve Code Quality**: Reduce requirement-related bugs by 50%
3. **Accelerate Onboarding**: Enable new developers to contribute effectively within 1 week
4. **Enhance Traceability**: Provide complete requirement-to-implementation visibility
### **Success Metrics**
- **Task Execution Time**: Reduce average task completion time by 25%
- **Context Clarity Score**: Achieve 90%+ developer satisfaction with task context
- **Bug Reduction**: 50% fewer requirement-related defects
- **Onboarding Velocity**: 70% faster new developer productivity ramp-up
---
## **Target Users**
### **Primary Users**
- **Software Developers**: Need comprehensive task context for efficient implementation
- **Technical Leads**: Require detailed task breakdown for accurate planning and review
- **Product Managers**: Need visibility into how business requirements translate to technical tasks
### **Secondary Users**
- **QA Engineers**: Benefit from enhanced acceptance criteria and testing context
- **DevOps Engineers**: Need technical constraints and deployment context
- **Project Managers**: Require accurate effort estimates and dependency understanding
---
## **Feature Requirements**
### **Core Context Generation**
#### **FR-1: Business Context Extraction**
**Description**: Extract and embed relevant business context into each task
**Requirements**:
- Parse PRD objectives and link to specific tasks
- Extract user impact statements and success metrics
- Identify business justification for each task
- Include relevant stakeholder information
**Acceptance Criteria**:
- Each task includes parent business objective reference
- User impact clearly articulated (who benefits and how)
- Business value quantified where possible
- Success metrics linked to task completion
#### **FR-2: Technical Context Analysis**
**Description**: Analyze and include technical constraints and architectural context
**Requirements**:
- Extract technical requirements from PRD and related documents
- Identify architectural decisions affecting the task
- List integration points and system dependencies
- Include data model and API specification references
**Acceptance Criteria**:
- Technical constraints clearly documented
- Architecture decisions referenced with rationale
- Integration points identified with interface specifications
- Data requirements specified with schema references
#### **FR-3: Implementation Guidance Generation**
**Description**: Provide AI-generated implementation recommendations and best practices
**Requirements**:
- Generate step-by-step implementation approach
- Recommend appropriate tools and technologies
- Identify common pitfalls and mitigation strategies
- Provide code examples and patterns where applicable
**Acceptance Criteria**:
- Implementation steps are actionable and specific
- Tool recommendations align with project technology stack
- Pitfalls include specific mitigation strategies
- Code examples are relevant and syntactically correct
### **Enhanced Task Structure**
#### **FR-4: Contextual References System**
**Description**: Create comprehensive reference system linking tasks to source materials
**Requirements**:
- Extract relevant PRD sections with context explanations
- Link to related features and user stories
- Reference technical specifications and API docs
- Include similar implementation examples
**Acceptance Criteria**:
- PRD excerpts include relevance explanations
- Feature relationships clearly mapped
- Technical spec references are accurate and current
- Example implementations are contextually appropriate
#### **FR-5: Enhanced Acceptance Criteria**
**Description**: Generate detailed, testable acceptance criteria with verification methods
**Requirements**:
- Create specific, measurable acceptance criteria
- Define verification methods for each criterion
- Categorize criteria by type (functional, technical, quality)
- Include priority levels for each criterion
**Acceptance Criteria**:
- All criteria are specific and measurable
- Verification methods are clearly defined
- Criteria categories align with testing strategies
- Priority levels support incremental delivery
#### **FR-6: Dependency Context Enhancement**
**Description**: Provide detailed context for task dependencies and integration points
**Requirements**:
- Explain why dependencies exist
- Describe what dependent tasks provide
- Include integration guidance and interfaces
- Identify opportunities for parallel work
**Acceptance Criteria**:
- Dependency rationale is clearly explained
- Integration interfaces are well-documented
- Parallel work opportunities are identified
- Blocking relationships include mitigation strategies
---
## **Technical Solutions**
### **Solution 1: AI-Powered Context Extraction (Recommended)**
#### **Architecture**
```typescript
interface ContextGenerationPipeline {
// Stage 1: Content Analysis
contentAnalyzer: PRDAnalyzer;
requirementExtractor: RequirementExtractor;
// Stage 2: Context Generation
businessContextGenerator: BusinessContextGenerator;
technicalContextGenerator: TechnicalContextGenerator;
implementationGuideGenerator: ImplementationGuideGenerator;
// Stage 3: Reference Creation
referenceGenerator: ContextualReferenceGenerator;
exampleGenerator: CodeExampleGenerator;
// Stage 4: Enhancement
taskEnhancer: TaskContextEnhancer;
qualityValidator: ContextQualityValidator;
}
```
#### **Implementation Approach**
1. **Multi-Stage AI Processing**: Use specialized AI prompts for different context types
2. **Template-Based Generation**: Leverage proven templates for consistent context structure
3. **Incremental Enhancement**: Build context progressively through multiple AI calls
4. **Quality Validation**: Validate generated context for completeness and accuracy
#### **Pros**
- ✅ Highly accurate and contextually relevant
- ✅ Scales with AI model improvements
- ✅ Handles complex, nuanced requirements
- ✅ Learns from feedback and improves over time
#### **Cons**
- ⚠️ Higher token usage and cost
- ⚠️ Dependent on AI service availability
- ⚠️ May require fine-tuning for specific domains
### **Solution 2: Hybrid Template + AI Approach**
#### **Architecture**
```typescript
interface HybridContextGeneration {
// Template-based extraction
templateEngine: ContextTemplateEngine;
patternMatcher: RequirementPatternMatcher;
// AI enhancement
aiEnhancer: ContextAIEnhancer;
qualityImprover: AIQualityImprover;
// Combination logic
contextMerger: TemplateAIMerger;
conflictResolver: ContextConflictResolver;
}
```
#### **Implementation Approach**
1. **Template-Based Foundation**: Use predefined templates for common context patterns
2. **AI Enhancement**: Enhance template output with AI-generated insights
3. **Pattern Recognition**: Identify common requirement patterns for template selection
4. **Intelligent Merging**: Combine template and AI outputs intelligently
#### **Pros**
- ✅ More predictable and cost-effective
- ✅ Faster generation for common patterns
- ✅ Fallback capability when AI is unavailable
- ✅ Easier to debug and maintain
#### **Cons**
- ⚠️ Less flexible for unique requirements
- ⚠️ Requires maintenance of template library
- ⚠️ May miss nuanced context
### **Solution 3: Traceability-Driven Context Generation**
#### **Architecture**
```typescript
interface TraceabilityContextGeneration {
// Traceability analysis
traceabilityAnalyzer: RequirementTraceabilityAnalyzer;
relationshipMapper: TaskRelationshipMapper;
// Context derivation
contextDeriver: TraceabilityContextDeriver;
impactAnalyzer: ChangeImpactAnalyzer;
// Enhancement
contextEnricher: TraceabilityContextEnricher;
gapFiller: ContextGapFiller;
}
```
#### **Implementation Approach**
1. **Traceability Foundation**: Use existing traceability matrix as context source
2. **Relationship Analysis**: Analyze task relationships to derive context
3. **Impact Mapping**: Map business impact through traceability chains
4. **Gap Identification**: Identify and fill context gaps using AI
#### **Pros**
- ✅ Leverages existing traceability infrastructure
- ✅ Ensures consistency with requirements
- ✅ Provides strong audit trail
- ✅ Integrates well with existing system
#### **Cons**
- ⚠️ Limited by traceability matrix quality
- ⚠️ May miss implementation-specific context
- ⚠️ Requires comprehensive traceability setup
---
## **Recommended Solution: Multi-Stage AI-Powered Context Generation**
### **Implementation Strategy**
#### **Phase 1: Core Context Generation (4 weeks)**
1. **Business Context Extractor**
- AI prompts for objective extraction
- User impact analysis
- Success metrics mapping
2. **Technical Context Analyzer**
- Architecture decision extraction
- Constraint identification
- Integration point analysis
3. **Basic Implementation Guidance**
- High-level approach recommendations
- Technology stack suggestions
- Common pitfall identification
#### **Phase 2: Enhanced References (3 weeks)**
1. **Contextual Reference Generator**
- PRD section extraction with relevance
- Feature relationship mapping
- Technical specification linking
2. **Code Example Generator**
- Pattern-based example generation
- Technology-specific snippets
- Best practice demonstrations
#### **Phase 3: Advanced Enhancement (3 weeks)**
1. **Enhanced Acceptance Criteria**
- Detailed criterion generation
- Verification method specification
- Priority and category assignment
2. **Dependency Context Enhancement**
- Dependency rationale explanation
- Integration guidance generation
- Parallel work identification
#### **Phase 4: Quality & Optimization (2 weeks)**
1. **Context Quality Validation**
- Completeness checking
- Accuracy validation
- Consistency verification
2. **Performance Optimization**
- Token usage optimization
- Caching implementation
- Parallel processing
### **Technical Implementation**
#### **Context Generation Service**
```typescript
export class TaskContextGenerationService {
async generateTaskContext(
task: AITask,
prd: PRDDocument,
config: ContextGenerationConfig
): Promise<EnhancedTaskContext> {
// Stage 1: Business Context
const businessContext = await this.generateBusinessContext(task, prd);
// Stage 2: Technical Context
const technicalContext = await this.generateTechnicalContext(task, prd);
// Stage 3: Implementation Guidance
const implementationGuidance = await this.generateImplementationGuidance(task);
// Stage 4: Contextual References
const contextualReferences = await this.generateContextualReferences(task, prd);
// Stage 5: Enhanced Acceptance Criteria
const enhancedCriteria = await this.generateEnhancedAcceptanceCriteria(task);
return {
businessContext,
technicalContext,
implementationGuidance,
contextualReferences,
enhancedCriteria
};
}
}
```
#### **AI Prompt Templates**
```typescript
export const CONTEXT_GENERATION_PROMPTS = {
businessContext: {
systemPrompt: `Extract business context for development tasks...`,
userPrompt: `Given this PRD and task, extract: business objectives, user impact, success metrics...`,
schema: BusinessContextSchema
},
technicalContext: {
systemPrompt: `Analyze technical requirements and constraints...`,
userPrompt: `Identify technical context: architecture decisions, constraints, integrations...`,
schema: TechnicalContextSchema
},
implementationGuidance: {
systemPrompt: `Generate implementation recommendations...`,
userPrompt: `Provide step-by-step implementation guidance...`,
schema: ImplementationGuidanceSchema
}
};
```
---
## **Configuration & Customization**
### **Context Generation Levels**
```typescript
export enum ContextLevel {
MINIMAL = 'minimal', // Basic business context only
STANDARD = 'standard', // Business + technical context
FULL = 'full' // Complete context with examples
}
```
### **Environment Configuration**
```bash
# Context Generation Settings
ENHANCED_TASK_GENERATION=true
CONTEXT_GENERATION_LEVEL=full
INCLUDE_BUSINESS_CONTEXT=true
INCLUDE_TECHNICAL_CONTEXT=true
INCLUDE_IMPLEMENTATION_GUIDANCE=true
INCLUDE_CODE_EXAMPLES=true
MAX_CONTEXT_TOKENS=2000
ENABLE_CONTEXT_CACHING=true
```
---
## **Success Criteria & Metrics**
### **Functional Requirements**
- ✅ 100% of generated tasks include business context
- ✅ 95% of tasks include relevant technical constraints
- ✅ 90% of tasks include actionable implementation guidance
- ✅ 85% of tasks include relevant code examples
### **Quality Metrics**
- **Context Completeness**: 95% of required context fields populated
- **Context Accuracy**: 90% accuracy validated through developer feedback
- **Context Relevance**: 85% of context rated as "highly relevant" by developers
- **Implementation Success**: 80% of tasks completed without additional context requests
### **Performance Metrics**
- **Generation Time**: < 30 seconds per task for full context
- **Token Efficiency**: < 2000 tokens per task context generation
- **Cache Hit Rate**: > 70% for similar context patterns
- **Error Rate**: < 5% context generation failures
---
## **Risk Assessment**
### **High Risk**
- **AI Service Dependency**: Mitigation through fallback mechanisms and caching
- **Token Cost Escalation**: Mitigation through optimization and configurable levels
### **Medium Risk**
- **Context Quality Variance**: Mitigation through validation and feedback loops
- **Performance Impact**: Mitigation through parallel processing and caching
### **Low Risk**
- **User Adoption**: Mitigation through gradual rollout and training
- **Integration Complexity**: Mitigation through modular design
---
## **Implementation Timeline**
| Phase | Duration | Deliverables |
|-------|----------|-------------|
| **Phase 1** | 4 weeks | Core context generation (business, technical, basic implementation) |
| **Phase 2** | 3 weeks | Enhanced references and code examples |
| **Phase 3** | 3 weeks | Advanced enhancement (criteria, dependencies) |
| **Phase 4** | 2 weeks | Quality validation and optimization |
| **Total** | **12 weeks** | **Complete AI-powered task context generation system** |
---
## **Conclusion**
The AI-Powered Task Context Generation feature will transform the development experience by providing comprehensive, actionable context for every generated task. This investment in developer productivity will pay dividends through reduced context-switching overhead, improved code quality, and accelerated team onboarding.
The recommended multi-stage AI approach provides the flexibility and accuracy needed to handle diverse requirements while maintaining performance and cost-effectiveness through intelligent optimization and caching strategies.