Context Window Chunking

Expert

Process document libraries that exceed context windows using sliding chunk schemas.

01 The Concept

When document libraries exceed the model's context window, sliding window chunking splits the text into overlapping segments, processes each chunk with local summarizations, and aggregates the results using a hierarchical map-reduce prompt structure.

02 Weak vs. Strong

EX 01Map-Reduce Chunking Pipeline (Python)

1. Split text into chunks with 200-word overlap

chunks = split_text(large_text, size=2000, overlap=200)

2. Map: extract facts in parallel

local_summaries = [call_llm(f"Extract facts: {c}") for c in chunks]

3. Reduce: compile final summary

final_summary = call_llm("Combine summaries:\n" + "\n".join(local_summaries))

→ Why it works

Systematically bypasses context constraints. Mapping extracts localized data, and reducing aggregates them, protecting pipelines from token crashes.

03 Key Points

01Sliding Windows: Splitting documents into segments with 10-20% overlap to preserve context.

02Map Step: Running local prompts on each chunk to extract key facts or summaries.

03Reduce Step: Fusing chunk summaries into a single cohesive final document.

04Chunk Overlap: Preventing loss of information at chunk boundaries.

05Context Limits: Enforcing systematic data flow when documents exceed model limits.

04 Model-Specific Notes

Claude handles local maps with high extraction precision. XML tag parameters help keep segment summaries distinct.

05 For Your Role

It's like summarizing a thick book chapter-by-chapter, and then writing a one-page summary based on those chapter notes.

Next: Negative Constraints & Guardrails →