Principle:Ucbepic Docetl Chunk Processing
| Knowledge Sources | |
|---|---|
| Domains | NLP, LLM_Operations |
| Last Updated | 2026-02-08 01:40 GMT |
Overview
An LLM transformation principle that processes each document chunk independently using a prompt template and structured output schema.
Description
Chunk Processing applies an LLM to each document chunk independently, extracting information, generating analyses, or transforming content according to a Jinja2 prompt template. Each chunk is processed in parallel, and results include the extracted fields defined by the output schema.
Key features include:
- Jinja2 Templating: Prompts reference chunk fields via Template:Input.field syntax
- Structured Output: JSON schema enforcement for consistent output structure
- Gleaning: Optional iterative validation rounds for quality improvement
- Batching: Process multiple chunks in a single LLM call for efficiency
Usage
Apply this principle after document splitting (and optionally gathering) when each chunk needs independent LLM processing. Common use cases include information extraction, summarization, classification, and analysis.
Theoretical Basis
Map-style parallel processing:
- Template Rendering: Fill Jinja2 prompt with chunk data
- LLM Invocation: Send rendered prompt to LLM with output schema
- Schema Validation: Validate LLM output against expected types
- Gleaning (Optional): Iteratively refine output through validation rounds
- Result Collection: Aggregate chunk-level results
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