Challenges Developing CT to Text in Production: Workflow Integration and AI Prompting

Moving our CT to Text pipeline from development to production revealed several challenges in workflow integration, data passing, and AI prompt engineering. Here’s how we tackled these challenges and created a robust production system.

Workflow Integration Challenges

1. Coordinating Multiple Workflows

The key challenge was getting two GitHub Actions workflows to work together seamlessly:

• Parse MorphoSource workflow: Extracts and processes CT data
• CT to Text workflow: Generates descriptive text using AI

# CT to Text workflow trigger
on:
  workflow_run:
    workflows: ["Parse MorphoSource Data"]
    types: [completed]

2. Data Passing Between Workflows

We needed to reliably pass the release body text from Parse MorphoSource to CT to Text:

- name: Fetch Latest Release
  id: fetch_release
  env:
    GITHUB_TOKEN: $
  run: |
    response=$(curl -sSL -H "Authorization: Bearer $GITHUB_TOKEN" \
      https://api.github.com/repos/$/releases/latest)
    body=$(echo "$response" | jq -r .body)
    echo "release_body<<EOF" >> "$GITHUB_OUTPUT"
    echo "$body" >> "$GITHUB_OUTPUT"
    echo "EOF" >> "$GITHUB_OUTPUT"

OpenAI Integration Challenges

1. Prompt Engineering

Creating an effective prompt for scientific content was crucial:

def generate_text_for_records(records):
    user_content = """Analyze these CT scan records and generate a scientific summary.
    Focus on:
    - Taxonomic classification
    - Anatomical features
    - Morphological significance
    - Research implications
    
    For each record, provide:
    - ~200 word description
    - Technical but accessible language
    - Highlight key findings
    
    Records:
    """
    
    for record in records:
        user_content += f"""
        Record #{record['record_number']}:
        - Title: {record['title']}
        - Object: {record['Object']}
        - Taxonomy: {record['Taxonomy']}
        """

2. Error Handling and Retries

We implemented robust error handling for API calls:

def call_openai_with_retry(prompt, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="gpt-4-1106-preview",
                messages=[
                    {"role": "system", "content": "You are a scientific writer..."},
                    {"role": "user", "content": prompt}
                ]
            )
            return response.choices[0].message.content
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)  # Exponential backoff

Release Management Challenges

1. Version Control and Naming

We implemented a systematic versioning approach:

- name: Generate Timestamp
  id: gen_ts
  run: |
    TS=$(date +'%Y-%m-%d_%H-%M-%S')
    echo "timestamp=$TS" >> "$GITHUB_OUTPUT"

- name: Create Release
  with:
    tag_name: "ct_to_text_analysis-$"
    release_name: "CT to Text Analysis #$"

2. Release Body Formatting

Ensuring consistent formatting of AI-generated text in releases:

def format_release_body(generated_text):
    sections = []
    for record in parse_text_into_records(generated_text):
        sections.append(f"""
## Record {record['number']}
{record['description']}

### Technical Details
- Object Type: {record['object_type']}
- Taxonomic Group: {record['taxonomy']}
        """)
    return "\n".join(sections)

Workflow Optimization Techniques

1. Intelligent Workflow Triggers

We implemented checks to prevent unnecessary processing:

- name: Check if morphosource-updates
  id: check_morpho
  run: |
    TAG_NAME="$"
    if [[ "$TAG_NAME" == morphosource-updates-* ]]; then
      echo "is_morphosource=true" >> "$GITHUB_OUTPUT"
    else
      echo "is_morphosource=false" >> "$GITHUB_OUTPUT"
    fi

2. Resource Management

Optimizing API usage and processing time:

def process_records_in_batches(records, batch_size=5):
    """Process records in smaller batches to manage resources"""
    batches = [records[i:i + batch_size] 
              for i in range(0, len(records), batch_size)]
    
    results = []
    for batch in batches:
        batch_text = generate_text_for_records(batch)
        results.extend(parse_generated_text(batch_text))
        time.sleep(1)  # Rate limiting
    
    return results

Best Practices Learned

1. Workflow Design
   • Clear dependency chains
   • Explicit error states
   • Comprehensive logging
2. AI Integration
   • Structured prompts
   • Consistent output formats
   • Error recovery mechanisms
3. Release Management
   • Systematic versioning
   • Clear naming conventions
   • Automated cleanup

Future Improvements

1. Enhanced AI Processing
   • More sophisticated prompts
   • Context-aware summaries
   • Multi-model verification
2. Workflow Optimization
   • Parallel processing
   • Caching mechanisms
   • Smarter retry logic
3. Release Management
   • Advanced version tracking
   • Automated quality checks
   • Change detection

Conclusion

Successfully integrating CT to Text processing in production required careful attention to workflow coordination, data passing, and AI prompt engineering. By implementing robust error handling, systematic versioning, and optimized processing patterns, we’ve created a reliable system for generating high-quality scientific summaries from CT scan data.

This foundation provides a solid base for future enhancements while maintaining the reliability needed for production use. As we continue to evolve the system, these core principles will guide our development of even more sophisticated CT data processing capabilities.

← Previous Post $~~~~~~~~~~~$ Next Post →