Batch Processing with AI

When you have dozens or hundreds of similar items to process, doing them one by one is inefficient. Batch processing lets you handle many items in a single AI interaction, dramatically multiplying your productivity. This lesson covers techniques for effective batch processing.

When to Use Batch Processing

Batch processing is ideal when you have:

• Multiple items requiring the same transformation
• Lists that need categorization or tagging
• Many documents needing the same type of analysis
• Repetitive formatting or conversion tasks
• Large datasets requiring cleanup or standardization

The Batch Processing Pattern

Basic Structure

Process each of the following items according to these rules:
[Clear, specific rules]

Output format for each item:
[Exact format you want]

Items to process:
1. [Item 1]
2. [Item 2]
3. [Item 3]
...

Key Elements

1. Numbered inputs: Makes it easy to match outputs to inputs
2. Clear rules: Exactly how each item should be processed
3. Consistent output format: Same structure for every result
4. Error handling: What to do with edge cases

Practical Batch Processing Examples

Example 1: Bulk Product Description Generation

Example 2: Batch Data Extraction

Example 3: Bulk Categorization

Handling Large Batches

AI has context limits. For very large batches:

Strategy 1: Chunk Your Data

Break large lists into groups of 10-20 items per request:

[Process first batch]
→ Save results
[Process second batch]
→ Combine results

Strategy 2: Progressive Processing

Start with a small sample to verify your rules work:

1. Test with 3-5 items
2. Review output quality
3. Adjust rules if needed
4. Process remaining items in chunks

Strategy 3: Use Output Templates

Define exact output format to make combining results easier:

Output as CSV format:
ID,Field1,Field2,Field3
1,value,value,value
2,value,value,value
...

Batch Quality Control

Include Verification Instructions

Error Flagging Pattern

Add instructions to flag problematic items:

Processing rules:
[Your rules]

If an item cannot be processed correctly:
- Still include it in output
- Add flag: [ERROR: reason]
- Continue with remaining items

Batch Processing Checklist

Before running a large batch:

• Test rules on 3-5 sample items
• Verify output format is exactly what you need
• Include error handling instructions
• Decide how to handle edge cases
• Plan how to combine results if chunking
• Set up quality check for a sample of outputs

Practice: Design Your Batch Process

Think of a task where you process many similar items. Design the batch:

Combining Batch Processing with Other Techniques

Batch processing works well with:

• Templates: Create template, then batch-apply to multiple recipients
• Format conversion: Convert many items between formats at once
• Categorization: Sort hundreds of items into categories quickly
• Extraction: Pull specific data from multiple documents
• Translation: Translate many items while maintaining consistency

Key Takeaways

• Number your items for easy tracking between input and output
• Test with small samples before processing large batches
• Include clear error handling instructions
• Chunk large batches to stay within AI context limits
• Use consistent output formats to make combining results easy
• Build in quality checks by flagging uncertain items
• Batch processing multiplies the value of every prompt pattern you learn