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