QuickToolPro
aiGenerator

RAG Chunking Tool

Split long text into token-estimated chunks with overlap for retrieval and AI knowledge bases.

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Generate result

Generator

Turn structured inputs into a usable draft.

Describe the goal, constraints, and format, then produce copy-ready output that can be refined or saved.

Brief

Describe the outcome, format, or constraints.

Generate

Create a structured draft or package.

Refine

Copy, edit, or save the output.

AI workflow settings

Paste source text, then choose chunk size and overlap in tokens.

Review chunk cards, token counts, and copy JSON or Markdown chunks.

Privacy: This tool runs entirely in your browser. No data is sent to our servers. We don't store, share, or have access to any of the information you process here.

Prompt, options, and generated output

Generated package

Turn prompt planning into a package you can copy into an AI tool, API request, or prompt library.

Budget
Token count, context use, chunk size, or cost estimate.
Prompt package
Structured instructions, constraints, examples, and output format.
Safety note
Injection risk, missing context, or model-routing checks.

Starter prompts and scenarios

How to generate a better first draft

Use RAG Chunking Tool when you need to split long text into token-estimated chunks with overlap for retrieval and AI knowledge bases.

It focuses on deterministic tasks such as token estimates, prompt structure, context budgets, chunking, costs, and structured-output instructions.

Common use cases

  • Prepare prompt, context, cost, or safety notes before using an AI workflow.
  • Estimate prompt size before using an AI model or API.
  • Prepare clean prompt sections, schemas, chunks, or cost notes for an AI task.

How to use it well

  1. Start in the tool area above and enter the smallest complete input that represents your task.
  2. Choose the model family, context limit, chunk size, price, or output format.
  3. Run the tool and review the structured estimate or generated template.
  4. Copy the result into your AI tool, app, documentation, or prompt library.

Practical tips

  • Treat token counts as model-family estimates unless you validate against the exact provider.
  • Reserve context for system instructions, tool messages, and expected output.
  • Keep secrets out of prompts you plan to share in documentation or tickets.

Limitations to know

  • These tools do not call an AI model and do not evaluate answer quality.
  • Model limits, tokenizers, and pricing can change, so verify critical production numbers with the provider.

Continue with a guide

AI Prompt Planning Guide

Token budget -> context plan -> chunks -> cost -> safety check.

Open guideOpen Prompt Lab
1

Count the prompt

Check whether the prompt leaves room for examples, retrieved context, and output.

AI Token Counter
2

Plan the context window

Reserve space for instructions, user content, tool messages, and final answer.

Context Window Planner
3

Shape retrieval chunks

Split long source material into overlap-aware chunks for RAG or knowledge workflows.

RAG Chunking Tool
4

Check safety and cost

Estimate API cost and scan untrusted text for prompt-injection patterns.

Prompt Injection Scanner

FAQ

Q: Does it create embeddings?

A: No. It prepares chunk text and metadata you can send to your own embedding pipeline.

Q: How should I choose chunk size?

A: Start with 400 to 800 tokens for general documents, then adjust based on retrieval quality and answer length.

Useful nearby tools

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Privacy: This tool runs entirely in your browser. No data is sent to our servers. We don't store, share, or have access to any of the information you process here.