# 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