The Tool Desk
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What Green built and what he was testing
Search Engine Journal, which published Green’s article on September 30, 2026, identifies him as Technical Director & Senior Consultant at Torque Partnership. He describes building Exactly Matchy, a Chrome extension intended to help assess whether content is retrievable by AI systems.
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His question was not whether a small model can reproduce ChatGPT or Claude on a laptop. It was how much useful work can move closer to the user, and which parts of that work really need a frontier model at all.
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Work that belongs in code
Green’s clearest conclusion is that some jobs have a single correct answer and should never be delegated to a generative model. Sitemap URL extraction and deduplication are his main examples. An XML parser and a short script are more predictable for these tasks than an LLM, and they produce output you can audit line by line.
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- Parsing: read the sitemap XML and extract every
<loc>value. - Normalizing: standardize URL formats so that equivalent addresses compare as equal.
- Deduplicating: collapse repeated URLs and keep a record of what was removed.
- Retaining evidence: store the raw inputs and the transformation steps so any result can be traced back to its source.
Where a local model fits, and where it fell short
The more interpretive case in Green’s work is comparing a page’s raw HTML with its rendered DOM. Differences between the two can reveal link problems that matter for AI retrievability. The types of evidence he describes include:
- links whose destination changed between the two versions;
- links whose anchor text changed;
- links that are broken in the initial HTML but work after rendering;
- different URLs that resolve to the same destination.
Green supplied this structured evidence to Gemini Nano, the on-device model behind Chrome’s Prompt API. In his experiment it was useful for some tasks but not reliable enough for a final decision he could trust. He reports that a stronger API model handled the same evidence considerably better.
These outcomes are qualitative and come from Green’s own experiment. The article does not include an independent replication or a numerical benchmark, and it does not report accuracy rates, so it is not possible to say how often the local model was right or wrong.
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The three-tier architecture
Green’s recommendation is a pipeline with three layers. Each layer does only the work it can do reliably.
- Compute exact facts in code. Parse XML, extract URLs, normalize values, deduplicate, and keep auditable evidence. A generative model should not be used for any operation whose correct result can be determined mechanically.
- Use local inference for limited interpretation. Ask a small model to summarize or classify structured evidence, where occasional errors can be checked and contained.
- Escalate hard judgments. Route ambiguous or consequential cases to a stronger model or a human reviewer. Keep the evidence and task interface stable, so the model can be swapped without redesigning the whole pipeline.
This is the article’s architectural recommendation. It is not a guarantee that any particular model will be accurate on any particular task. Green puts the principle this way:
“The opportunity isn’t to recreate ChatGPT or Claude locally. It is to build software where: Exact computation happens in code, lightweight intelligence happens locally, and expensive intelligence is called only when it is truly needed.”
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Choosing the right layer for a task
The table below applies Green’s pattern to common tasks in a link or retrievability audit. The “Best fit” column reflects his architecture, and the reasons are drawn from his experiment rather than from separate testing.
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| Task | Best fit | Why |
|---|---|---|
| Extracting URLs from sitemap XML | Code (XML parser) | The correct output is mechanically determinable. |
| Deduplicating and normalizing URLs | Code (script) | Equivalent inputs should always produce the same result, with an audit trail. |
| Triaging link differences between raw HTML and rendered DOM | Local model, with checks | Errors are tolerable and can be verified against the structured evidence. |
| Deciding whether a flagged issue needs a fix | Stronger remote model or human reviewer | Green found the local model unreliable for final decisions in his experiment. |
When you compare options for your own pipeline, weigh task type and the cost of an error, whether the answer can be computed deterministically, quality on your actual evidence, hardware availability, latency, privacy and data-handling boundaries, and whether a hard case can be escalated. The sources do not quantify cost or speed for any of these options, so any numerical comparison would need to come from your own measurements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Chrome’s Prompt API: what it requires
Chrome for Developers’ Prompt API documentation, last updated August 26, 2026, states that the API uses Gemini Nano. The requirements below are Chrome-specific. They are not general requirements for every local-model runtime, and they may change as Chrome updates the model.
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| Requirement | Chrome Prompt API, per the documentation dated August 26, 2026 |
|---|---|
| Supported desktop environments | Windows 10 and 11; macOS 13 and later; Linux; ChromeOS on Chromebook Plus devices |
| Not supported (as of that date) | Chrome for Android and iOS; ChromeOS on non-Chromebook Plus devices |
| Free storage | At least 22 GB |
| Hardware, GPU path | A GPU with strictly more than 4 GB VRAM |
| Hardware, CPU path | A CPU with at least 16 GB RAM and at least four cores |
Either the GPU path or the CPU path is sufficient. A machine with 16 GB of RAM is therefore one qualifying route, not the only one, and it must still meet the operating-system and storage requirements.
Download, offline use and privacy
The same documentation says the model is downloaded separately on first use. The initial download requires an unmetered connection. After the download, using the model does not require a network connection.
Google’s documentation states: “No data is sent to Google or any third party when using the model.” That statement covers Chrome’s built-in Prompt API. It does not describe how an individual extension handles its own networking, logging, storage, or telemetry. If your extension sends data anywhere, that behavior must be documented and assessed separately.
Bottom line for builders
Start by moving every deterministic step into code, then test whether a small local model improves triage on your real evidence. Keep a stronger model or a person in the loop for decisions that matter. Verify the Chrome requirements against the current documentation before you promise any user that the feature will run on their machine.
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