3.9 KiB
3.9 KiB
Intake Processing Workflow
Overview
This workflow describes the process for converting intake responses into synchronized human and LLM formats.
Workflow Steps
1. Response Collection
- Receive completed intake templates
- Validate completeness and basic formatting
- Store in
responses/directory with timestamp and identifier - Create processing ticket/task in tracking system
2. Initial Processing
- Parse structured response data
- Identify sections requiring human review
- Flag inconsistencies or unclear responses
- Generate initial conversion drafts
3. Human Review and Validation
- Review parsed data for accuracy
- Validate against existing databank information
- Resolve flagged issues and ambiguities
- Approve or reject conversion drafts
4. Format Conversion
- Convert validated data to human-friendly markdown
- Convert validated data to LLM-optimized structured formats
- Generate cross-references and links
- Apply formatting standards and conventions
5. Synchronization
- Update both
../human/and../llm/directories - Maintain version history and change tracking
- Update README and index files as needed
- Validate synchronization integrity
6. Quality Assurance
- Verify formatting consistency
- Check cross-reference integrity
- Validate change tracking accuracy
- Confirm synchronization between formats
7. Documentation and Notification
- Update processing logs and metrics
- Notify stakeholders of updates
- Archive processing artifacts
- Close processing tickets/tasks
Automation Opportunities
Parsing and Validation
- Automated YAML/JSON schema validation
- Consistency checking against existing data
- Completeness verification
- Basic formatting normalization
Format Conversion
- Template-driven markdown generation
- Structured data serialization
- Cross-reference generation
- Index and navigation updating
Synchronization
- Automated file placement and naming
- Version tracking table updates
- Conflict detection and resolution
- Integrity verification
Manual Review Requirements
Complex Judgments
- Interpretation of ambiguous responses
- Resolution of conflicting information
- Quality assessment of converted content
- Approval of significant changes
Creative Tasks
- Crafting human-friendly explanations
- Optimizing LLM data structures
- Designing intuitive navigation
- Balancing detail and conciseness
Quality Gates
Gate 1: Response Acceptance
- Response received and stored
- Basic formatting validated
- Completeness verified
- Processing ticket created
Gate 2: Data Validation
- Structured data parsed successfully
- Inconsistencies identified and flagged
- Initial drafts generated
- Review tasks assigned
Gate 3: Human Approval
- Manual review completed
- Issues resolved
- Conversion drafts approved
- Quality gate checklist signed off
Gate 4: Format Conversion
- Human-friendly markdown generated
- LLM-optimized formats created
- Cross-references established
- Formatting standards applied
Gate 5: Synchronization
- Both directories updated
- Version tracking maintained
- Integrity verified
- Change notifications prepared
Gate 6: Quality Assurance
- Formatting consistency verified
- Cross-reference integrity confirmed
- Change tracking accuracy validated
- Final approval obtained
Metrics and Tracking
Processing Efficiency
- Time from response receipt to completion
- Automation vs. manual effort ratio
- Error rate and rework frequency
- Stakeholder satisfaction scores
Quality Measures
- Accuracy of parsed data
- Completeness of converted content
- Consistency between formats
- User feedback and adoption rates
Continuous Improvement
- Bottleneck identification and resolution
- Automation opportunity tracking
- Process optimization initiatives
- Skill development and training needs