Mastering LLM Chunking: Efficiently Summarize Long Documents

Introduction: The Token Limit Trap

Do you dread feeding massive reports into an LLM only to hit a hard token ceiling—and lose half your context? Manual copy-pasting, frantic trimming, and endless API retries waste your time and budget. It’s time for a smarter, repeatable workflow.

Mastering LLM Chunking: Efficient Document Summaries

Many of us struggle to feed long reports into an AI without hitting token limits or losing important context. Chunking solves this by breaking your document into smaller pieces, summarizing each piece, and then stitching the summaries back together. Here’s how to do it in simple steps:

1. Split with overlap– Take your full document (for example, 10,000 tokens) and cut it into 1,000-token chunks.– Include a small overlap (say, 200 tokens) between chunks so you don’t lose sentences that cross the cut.

2. Label each chunk– At the top of each chunk add a clear tag like “Chunk 3 of 10.”– This tells the AI where it sits in the sequence and keeps things organized.

3. Summarize and save– Ask the AI to summarize each chunk in 3–5 bullet points.– Save each summary with its chunk number in a simple list or JSON file.

4. Combine into one summary– Once you have all chunk summaries, send one final prompt that lists them all and asks the AI to merge, remove duplicates, and polish into a single executive summary.

   
   

Why this matters at Singularity

• Fast report summaries – turn 50-page PDFs into concise overviews in seconds.

• Code Q&A – feed large codebases in chunks so you never miss a function or TODO.

• Compliance checks – chunk at logical boundaries and highlight key regulation points.

Every summary we produce is quickly reviewed by a human for accuracy and clarity.

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