Feeding long documents into AI sounds simple.
Paste in a giant report, hit go, and sit back while the machine does the heavy lifting… right?
Not so fast.
If you’ve tried it, you already know the pain:
AI gives you a random wall of text.
It skips important details.
Or it just… forgets half of what you told it.
Here’s why: context windows.
Every AI model has a limited memory box — the “context window.”
Short windows (2k–8k tokens): Easy to overflow. AI forgets key details if the text is too long.
Long windows (32k–200k tokens): Can hold a lot more, but they’re pricier and AI still gets confused if you dump in irrelevant content.
💡 Research (AI21, Snowflake, Pinecone, Weaviate) all point to the same fix:
Break the text into meaningful chunks → summarize each → then combine them.
That’s what the CUTS Framework is all about.
📝 The CUTS Framework
A simple method to keep long text under control:
C — Chunk
Split the text into smaller sections (~800–1,800 characters, or 1–2 pages). Overlap a few lines between chunks so nothing gets lost.
U — Understand
Summarize each chunk separately, using a consistent style (e.g., 3 bullet points).
T — Tag
Give each chunk a label or metadata:
C1: Budget IssuesC2: Customer Feedback
S — Summarize Again
Take all the summaries and feed them back in to create one master digest.
🔑 Example Prompt
Step 1 – Summarize Each Chunk
You are a summarization assistant.
TASK
Summarize the following text into 3 bullet points.
RULES
- Be concise and factual.
- Return ONLY valid JSON.
OUTPUT SCHEMA
{
"chunk_id": "C1",
"summary": ["point1", "point2", "point3"]
}
TEXT
<<<paste first section of your doc here>>>
Step 2 – Combine All Summaries
Combine the following JSON summaries into one master summary with 5 key points.
RULES
- Be concise and factual.
- Return ONLY valid JSON.
OUTPUT SCHEMA
{
"summary": ["point1", "point2", "point3", "point4", "point5"]
}
SUMMARIES
<<<paste all chunk outputs here>>>
⚡ Try This Today (Your Quick Win)
Grab one long email thread, report, or transcript you’ve been putting off.
Break it into 2–3 chunks.
Summarize each chunk with the JSON prompt.
Combine them into a master summary.
✅ In under 10 minutes, you’ll have a clear, digestible overview.
🛠 Pro Tips (From Research)
Know your model’s window. Assume 8k tokens unless you’ve checked.
Chunk by meaning, not just size. Use sections or headings as natural breakpoints.
Add metadata. Title, date, and author tags help sharpen the final summary.
Leave buffer. Don’t max out tokens — save space for instructions.
Validate JSON. Use a quick checker like jsonlint.com.
📚 Where CUTS Makes a Difference
This isn’t theory — here’s where context windows + chunking actually help:
Meeting & call transcripts → turn 60 minutes into a 1-page recap
Customer support tickets → condense long threads into action summaries
Contracts & legal docs → chunk by section (Terms, Payment, Termination)
Research papers → split by headings; combine into quick digests
Content repurposing → turn ebooks/blogs into bite-sized posts
Knowledge bases → chunk docs so answers stay accurate
Finance & compliance → extract risks & numbers from filings
Healthcare notes → summarize long case histories by section
CRM & sales notes → condense account histories into exec briefs
Project docs → break strategy decks into milestone-based summaries
✅ Anytime text feels too long to read (or reuse), CUTS makes it manageable.
👀 Want to See CUTS in Action?
Reading about it is one thing — but the real magic is putting it to work.
If you want to see the CUTS Framework in action (and try it on your own text)…
👉 Sign up to Agent Foundry and upload your first doc.
You’ll get a ready-to-use setup that chunks, summarizes, and delivers clean digests automatically — no code required.