Planner Inputs
Example Data Table
| Scenario | Context window | Text tokens | Chunk budget | Overlap | Chunks |
|---|---|---|---|---|---|
| Long document summarization | 8,192 | 12,000 | 1,620 | 120 | 8 |
| RAG evidence packing | 16,384 | 25,000 | 2,700 | 200 | 10 |
| Chat history condensing | 4,096 | 5,500 | 1,050 | 90 | 6 |
Values are illustrative; your results depend on reserves, headers, and safety margin.
Formula Used
- Effective context = Context window − System reserve − Output reserve
- Raw chunk budget = min(Max chunk cap, Effective context − Header tokens)
- Chunk budget = floor(Raw chunk budget × (1 − Safety margin %))
- Stride = max(1, Chunk budget − Overlap)
- Chunks = ceil((Text tokens − Overlap) / Stride), when Text tokens > 0
How to Use This Calculator
- Pick a context window that matches your deployment target.
- Enter total text tokens, or switch to word count mode.
- Set reserves for system instructions and expected output.
- Add header tokens if you prepend labels to each chunk.
- Choose overlap to maintain continuity between chunks.
- Use a safety margin to prevent edge-case overflow.
- Click Generate Plan, then export CSV or PDF if needed.
Context window budgeting in production
Modern models accept context windows, such as 4k, 8k, 16k, or 32k tokens. In real deployments, you never get the full window for user content because system instructions, tool traces, and expected output consume space. A reserve of 15–35% is common when responses must be consistent. For instance, reserving 350 system tokens and 700 output tokens leaves 7,142 tokens inside an 8,192 window.
Estimating tokens from words
When token counts are unknown, teams estimate from word count. For English prose, 1.1–1.6 tokens per word is typical, while code and dense symbols trend higher. This planner converts words to tokens using your chosen rate so chunk sizing decisions remain comparable across datasets.
Choosing a chunk size and safety margin
Chunk size should stay below the effective context after reserves and per‑chunk headers. Safety margins reduce overflow risk from tokenization variance, added metadata, and dynamic prompts. Many teams start with 5–15% safety and tighten it only after observing stable logs across hundreds of runs. A 10% margin on a 1,800 budget produces 1,620 content tokens, matching long‑document workflows.
Overlap trade‑offs and repetition cost
Overlap preserves continuity for entity references, tables, and multi‑step reasoning, but it repeats content. For example, an overlap of 200 tokens with a 2,000‑token budget yields a 1,800‑token stride; ten chunks repeat roughly 1,800 tokens, increasing total usage and latency overall. Tracking repeated tokens helps optimize both quality and spend. In evaluation, compare answer accuracy versus added tokens to find the smallest overlap that maintains coherence.
Interpreting the generated plan
The plan lists start and end token offsets, content tokens per chunk, and an estimated total token load per request. Use the table to align chunk boundaries with natural sections, then export CSV for review or PDF for stakeholders. Re‑run the calculator when changing prompts, adding retrieval citations, or adjusting response length targets. If chunks exceed 20, consider summarizing earlier sections to reduce downstream accumulation.
FAQs
What does the chunk budget represent?
It is the maximum content tokens you can place in each chunk after subtracting reserves, header tokens, and the safety margin from your context window.
How should I choose overlap tokens?
Start with 5–15% of the chunk budget. Increase overlap for tightly connected sections, or reduce it to cut repeated tokens when documents are well structured.
Why reserve tokens for output?
If you do not reserve output space, the model may truncate its answer. Reserving tokens keeps generation predictable and prevents the request from exceeding the context limit.
Is word mode accurate for non‑English text?
It is a rough estimate. Tokenization varies by language and punctuation density, so measure a representative sample and adjust the tokens‑per‑word setting for your content.
What is header tokens per chunk?
Header tokens represent repeated instructions or labels you prepend to every chunk, such as task rules, formatting constraints, or chunk identifiers used for traceability.
When should I re-run the planner?
Recalculate when you change prompts, add retrieval citations, modify expected response length, or switch models, because each change alters reserves, effective context, and chunk count.