Does this tool send my document to external LLM APIs?

No, all metrics are calculated locally using deterministic offline heuristics.

What counting units are supported?

You can count chunks using characters, words, or approximate tokens.

How is the overall score calculated?

It is the mean of four heuristic scores: coherence, coverage, overlap health, and chunk-size consistency.

What is the difference between the splitting methods?

Fixed-size cuts text strictly at the limit, sentence-aware snaps to sentence boundaries, and paragraph-aware snaps to paragraph ends.

Can I view the actual chunks generated by the best scheme?

Yes, switch the render view to 'Best scheme detail' to inspect the individual chunks and boundary annotations.

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AI RAG Chunk Quality Scorer

Score candidate RAG chunk-splitting schemes for a document across four metrics — coherence (clean sentence/paragraph boundaries), coverage (topic focus / key-term concentration), context overlap health, and chunk-size consistency — then compare up to three schemes side by side and recommend the best. Pure offline heuristics, no model calls.

Details

What this tool helps you do

A Retrieval-Augmented Generation (RAG) chunking evaluator for AI engineers and documentation QA teams:

Document — paste the source text you plan to index.
Counting unit & method — count chunks in characters, words, or ≈tokens, and split either at fixed size (may cut mid-sentence), sentence-aware (snap to sentence ends), or paragraph-aware (snap to paragraph ends).
Three candidate schemes — set a chunk size and overlap for schemes A, B, and C. These are the configurations you want to compare.
Scoring — each scheme is scored 0–100 on coherence, coverage, overlap, and consistency; the overall score is the mean.
Recommendation — the tool picks the highest-scoring scheme, shows a side-by-side scorecard, and breaks the best scheme into its chunks with boundary annotations.

All metrics are deterministic heuristics computed locally — no external API, no model calls — so results are reproducible and cheap to iterate on.

Execution

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Prepared example runs

Click an example to fill the form automatically. File inputs still need an upload.

2 examples

Compare three chunk sizes for a technical doc

A short technical passage scored with schemes of 256/512/1024 tokens (sentence-aware, 12.5% overlap) to pick the best size.

<div class="rag-wrap"><section class="rag-panel"><h3>🏁 Scorecard</h3>…A / B / C compared, best highlighted…</section></div>

Fixed-size vs sentence-aware coherence

Same document, method switched to fixed-size to show the coherence penalty of cutting mid-sentence.

<div class="rag-wrap">…best scheme chunks shown with “cut mid-sentence” boundary tags…</div>

Inputs

Options are grouped so long tools stay easier to scan.

11 options

ContentPaste or type the main input values.1

Document texttextareaRequired

SettingsAdjust formats, ranges, numbers, and modes.10

Counting unitselectOptionalSplitting methodselectOptionalScheme A — sizenumberRequiredScheme A — overlapnumberOptionalScheme B — sizenumberRequiredScheme B — overlapnumberOptionalScheme C — sizenumberRequiredScheme C — overlapnumberOptionalRender viewselectOptionalDecimal placesnumberRequired

Result

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Run the tool to preview files, text, structured data, or streamed output here.

Samples

AI RAG Chunk Quality Scorer

What this tool helps you do

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Prepared example runs

Inputs

Result

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Prepared example runs

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Key facts

Overview

When to use

How it works

Use cases

Examples

1. Optimizing Technical Manual Chunking

2. Preventing Mid-Sentence Cuts in Knowledge Bases

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