LLM Chunking Simulator

Introduction

AI systems chop up brand content during the Retrieval-Augmented Generation (RAG) phase!

Ahrefs, a leading marketing SaaS platform, published an extensive July 9, 2026 blog article discussing how the AI chunking and RAG process unfolds in real-time.

This 100% free simulation tool is designed to help keep your brand name semantically connected, to your most valuable website content!

Interactive LLM “Chunking” Simulator

*** Please review important Terms of Use and Disclaimer relevant to this software application ***

(See explanatory release notes below)

LLM “Chunking” Simulator (by Dino D. Romanelli)

When your brand or entity name is omitted from an AI model’s reference block, semantic context can be lost entirely!

Use this interactive LLM simulator to test how the RAG pipeline retrieval process impacts the contents of your webpages, blog articles, landing pages, whitepapers, product pages, social media posts, etc.

ENTER BRAND (TARGET ENTITY) NAME

Enter Document Source Introducing “Mindful.” The XYZ Company’s new CRM software suite. It was designed with busy SMEs in mind. Costs are $49 per month. It integrates well with Slack, Outlook, HubSpot, Zendesk, DocuSign, and Zapier. Customer support is 24/7. Try it risk-free today (2-week trial offer).

Total Characters (With Spaces): 280

Activate Semantic Chunking

No

Activate Semantic Overlap

No

Simulated Chunk Window (Character Limit)

Preselected Character Chunk Windows

250 500 750 1000 1500 2000 2500 3000 3500 4000

Reset Document Source

---

Release Notes (Version 1.0.2 – Beta)

1. This is a simulator designed to mimic as closely as possible, how AI systems parse your web content during the search extraction process. There are strict limits on how much text an LLM (large language model) like ChatGPT, Gemini, and Claude can process. This parsing phase is necessary so that LLMs can more easily process, store, and analyze content.
2. The ideal users of this tool would be professionals in the following industries: SEO, AEO, GEO, Social Media Marketing, eCommerce, Content Marketing, Brand Management, UX/UI Design, Web Development, Pay Per Click (PPC), Journalism, and Online Publishing.
3. The fields have been pre-populated with a sample entity (Mindful), and a sample passage (comprised of 280 characters). These are the default settings for the simulation tool, and can be overridden by your customized inputs.
4. The “Enter Brand (Target Entity) Name” field is designed for you to type or paste brand names and organization names you wish to evaluate for semantic relevance.
5. The “Enter Document Source” field allows you to type or paste a digital asset (e.g., webpage, blog article, social media post, etc.) you wish to evaluate for semantic relevance. There is a dynamic counter – “Total Characters (With Spaces)” – that will keep track of how many characters (including spaces) appear in the document source.
6. “Activate Semantic Chunking” – AI systems break apart content using two primary methods: fixed-size chunking or semantic chunking. There is a “Yes/No” toggle switch for you to choose whether or not you would like to have your Chunk Blocks split based upon natural punctuation, full sentences, and paragraph breaks (“Yes” = semantic chunking), or whether you would like to have them split in the middle of a natural word or sentence, based upon a fixed-size (“No” = fixed-size chunking).
7. “Activate Semantic Overlap” – AI systems also factor in “overlapping” text whenever they create chunking segments. Overlapping occurs when chunks share a portion of their text, with adjacent chunks. This ensures that no vital information is lost at the boundaries. There is another “Yes/No” toggle switch for you to select whether or not you would like to have overlapping text shown in the respective chunk blocks. The default overlap percentage for this setting is ~15% text overlap, between the respective chunk blocks. Overlap typically averages between 10% – 20% during AI retrieval.
8. See the semantic features in action: First, toggle the “semantic chunking” button to the “On” position, and observe how the default text changes in the respective chunk windows. Secondly, toggle the “semantic overlap” button to the “On” position, and observe how the default text changes even further in the respective chunk windows. This is the general format that the AI models follow to retrieve and store your brand information!
9. The “Simulated Chunk Window (Character Limit)” field can be dynamically adjusted to any character segment, from 25-character chunks up to 4000-character chunks. There is a slider, predesignated radio buttons, and a fillable number display, all of which can be used to dynamically select the desired chunk segment you wish to analyze.
10. The “Reset Document Source” will allow you to reset everything back to default settings.
11. The “Standard LLM Chunk Sizes (In Characters)” area provides estimates of typical AI model chunking segments, based upon the various document types.
    • Chunking Calculation Formula: (Word Count In Your Document Source x 5.5 Characters) / Average Chunk Size for Document Type = Projected Number of Distinguishable Chunks.
    • The average English word consists of 5.5 characters (a standard linguistic rule). A typical 3,000-word blog post would likely be broken into an average chunk size of 1,500 characters. Therefore, you would expect that the AI model would break your content up into at least 11 distinguishable chunks during the retrieval process.
    • E.g., (3,000 words x 5.5 Characters) / 1,500 Characters = 11 Distinguishable Chunks.
12. There is a dynamic bar at the top that will let you know the percentage of character chunks that don’t have any reference to your targeted “entity” (e.g., brand name). The default setting will show as follows: “67% of chunks have no entity reference”
13. Each dynamic “character chunk” will be designated as “Chunk Block 1”, “Chunk Block 2”, “Chunk Block 3”, an so on. The number next to each one of these designations will indicate the exact character count (including spaces) in each chunk segment.
14. On the bottom of the simulator is a dynamic “Suggested Rewrite” feature, which will analyze any entity (brand) gaps in your original text that were caused by chunking, and then suggest how you might rewrite your original text so that your brand remains semantically connected to important high-value content during the chunking phase of the AI retrieval process! Note: There is a “Copy Text” icon on the top right in order for you to copy/paste the suggested contents into a word processing format, such as Google Docs or MS Word.
15. There is no such thing as a perfect score. You should try to preserve the semantic reference in as many of the representative chunking segments as reasonably possible, so that the bulk of your content does not show any “Context Severed” warnings in the respective blocks.
16. This tool can also be used to analyze how LLMs might process your competitor’s content!
17. This interactive LLM Chunking Simulator tool is currently in Beta release.
18. Any feedback may be submitted through our Contact page. Thank you.

---