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Prompt and RAG quality

Prepare RAG Chunks Before Indexing

Score RAG chunks, prompt injection risk, output schema, context pressure, similarity, and hallucination risk before adding documents to an AI agent workflow.

Best for: developers, AI operators, support bot owners, publishers, and teams building retrieval-augmented generation workflows.

rag chunk qualityprepare rag chunksprompt injection scannerrag quality checkerllm output schema extractorembedding similarity checker
Start with RAG Chunk Quality Scorer Build Smart Run Sheet Build Proof Passport Ask Tool Finder Ask Answer Engine

Fast route that actually finishes the job

Start with RAG Chunk Quality Scorer. The supporting tools are included only when they make the output more trustworthy: conversion, cleanup, compression, preview, or verification. The goal is a checked artifact, not a long tour through a tool directory.

RAG Chunk Quality ScorerPrimary starting point for this intent. Use it first and check the baseline output before adding steps. Prompt Injection ScannerUse only if this step verifies, converts, prepares, or reduces the final artifact. Embedding Similarity ExplorerUse only if this step verifies, converts, prepares, or reduces the final artifact. LLM Output Schema ExtractorUse only if this step verifies, converts, prepares, or reduces the final artifact. Context Window BudgeterUse only if this step verifies, converts, prepares, or reduces the final artifact. AI Hallucination Likelihood ScorerUse only if this step verifies, converts, prepares, or reduces the final artifact. Prompt and RAG Quality KitUse only if this step verifies, converts, prepares, or reduces the final artifact.

Safe sample and expected output

Safe sample input

Five redacted help-center chunks, one system prompt, one user task, one expected JSON output, and one answer with named products, dates, prices, and no citations.

Expected output

A prompt and RAG quality brief that flags unsafe instructions, weak or duplicate chunks, missing schema fields, context pressure, unsupported claims, and safer next actions.

SMART RUN SHEET

Plan the run before touching the final file

This is the pre-flight layer most utility sites skip. Tell FastTool what you are trying to finish, how sensitive the input is, and what device you are using. The page returns a local readiness score, risk warning, first tool, and proof plan before you risk the real file.

Run readiness0/100Calculating

Warnings

  • Calculating.

Generated plan

  1. Calculating.
Open RAG Chunk Quality Scorer

          

          

            
Proof checks before you trust it

            
Use this checklist before you send, upload, publish, or reuse the output. If you cannot verify the result, do not treat it as finished.

            
  1. Reject retrieved text that tries to override instructions or control tools.
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  2. Score whether each chunk is self-contained, answerable, well-sized, and entity-preserving.
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  3. Find near-duplicate chunks before they pollute retrieval.
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  4. Define a schema or output contract before wiring model output into automation.
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  5. Mark unsupported named claims as verification work before publishing or acting on them.

          

          

            
PROOF PASSPORT

            
Create a local verification receipt

            
This is the part most tool sites skip. Check the output, record the file or result you created, and copy a proof receipt for your notes, ticket, client handoff, or repeat workflow. Nothing is uploaded; this runs in your browser.

            

              




            

            

              
              
              
            

            
0/5 checks passed

            

            

          

          

            
Common mistakes this route avoids

            
  • Indexing every paragraph without checking retrieval value.
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  • Testing only prompts while ignoring retrieved data quality.
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  • Letting retrieved documents contain instructions for the agent.
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  • Overfilling context until important instructions are dropped.
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  • Accepting structured output without a schema or required-field check.

          

          

            
Decision table

            

              
NeedUseCheck before done

              
                
First usable outputRAG Chunk Quality ScorerA prompt and RAG quality brief that flags unsafe instructions, weak or duplicate chunks, missing schema fields, context pressure, unsupported claims, and safer next actions.


Supporting verificationPrompt Injection ScannerScore whether each chunk is self-contained, answerable, well-sized, and entity-preserving.


Supporting verificationEmbedding Similarity ExplorerFind near-duplicate chunks before they pollute retrieval.


Supporting verificationLLM Output Schema ExtractorDefine a schema or output contract before wiring model output into automation.


Supporting verificationContext Window BudgeterMark unsupported named claims as verification work before publishing or acting on them.


Supporting verificationAI Hallucination Likelihood ScorerMark unsupported named claims as verification work before publishing or acting on them.


Supporting verificationPrompt and RAG Quality KitMark unsupported named claims as verification work before publishing or acting on them.

              
            

          

          

            
When not to use this workflow

            
Not for bypassing model safeguards, production security approval, regulated compliance sign-off, legal or medical fact checking, or approving a private agent architecture by itself.

            
Privacy boundary

            
Use redacted chunks, safe samples, and synthetic outputs. Do not paste secrets, credentials, private tickets, medical records, payment data, or regulated customer records.

          

          

            
Why this is built for repeat visits

            
A returning visitor should not have to remember which of hundreds of utilities solves the job. This page keeps the exact intent, starting tool, supporting checks, sample, expected output, and stop condition on one stable URL.

            
The useful end state is simple: open the right tool first, protect private inputs, verify the artifact, and stop once the output passes the visible proof checks.