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CaptionBot is a small tool built to help Nigerian clothing sellers turn an item description into a caption they can copy or send to WhatsApp. Its author describes a workflow that uses local AI to draft the caption, with important limits: voice input relies on Google’s servers, generated details need checking, and the tool had not yet been tested with sellers.
What CaptionBot is designed to do
CaptionBot focuses on one recurring task: writing a fresh sales caption for each clothing item advertised through WhatsApp Status. It is not presented as a general-purpose AI writing assistant. The aim is to take a seller’s item details, produce a clear caption, and make it easy to copy or send to WhatsApp.
The project article illustrates the input with: “I get two Ankara gowns, size 12 and 14, 18k each, Lagos delivery.” That is an example from the author, not evidence of a commonly used phrase or of performance across Nigerian sellers. Benny’s CaptionBot project article
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How the described workflow works
- Enter or speak the item details. The author describes typed input and Chrome voice typing as ways to provide a description.
- Prepare the text. A small preprocessing step is intended to address common voice-transcription mistakes.
- Generate a draft locally. The application uses Ollama to run Gemma 3 1B, which generates the caption. Google describes the 1B Gemma 3 model as text-to-text and intended for mobile devices and single-board computers; its documentation lists Ollama among frameworks for running Gemma models. This supports the plausibility of the model/runtime pairing, but does not independently confirm CaptionBot’s implementation or performance. Google Gemma model documentation
- Check the result. Application code checks the generated caption before presenting it, according to the author.
- Hand it off. The seller can copy the caption or use the option to send it to WhatsApp.
The author says the model download in this implementation was about 815 MB, as reported in the 2026 project article. That is an implementation-specific figure, not a verified current download size or a general hardware requirement.
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Why the author added price checks
The first version reportedly produced a caption with “40k” even though the input description had no price. The author attributed this to the model copying a 40k lace-gown example in its prompt. In response, the author says they removed misleading examples, strengthened the instruction not to invent details, added code to check a generated price against the supplied input, and set the application to retry up to three times when validation fails. These are the author’s account of changes; the repository and the effectiveness of those checks were not independently audited.
The incident illustrates a practical risk in AI-generated sales copy: a polished sentence can still contain a fabricated price or other unsupported product detail. A check that looks for price mismatches addresses one specific failure mode; it does not establish that every detail in every caption is accurate. Sellers should verify the caption against the item information before sharing it.
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Voice input, Pidgin, and the privacy boundary
The author reports that Chrome voice typing sometimes misheard “lace” as “list” and “12k” as “12 key.” They also found that it struggled with Nigerian Pidgin in some cases, while typed Pidgin worked much better. These are the author’s observations, not results from a measured language benchmark.
Although caption generation is described as running locally through Ollama and Gemma, voice typing uses Google’s servers. As a result, the full input-to-caption process is not entirely local or offline. Someone who does not want spoken item details sent through that voice-typing service can type the description instead, though the author’s experience with typed Pidgin should not be treated as a guarantee of accuracy for every seller.
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What remains unproven
The author says CaptionBot had not yet been put in a seller’s hands and does not claim user validation. A working author-tested flow is therefore not proof that sellers adopt it, save time with it, or find its captions effective. The author identifies several questions for real-world use:
- Whether sellers prefer speaking or typing descriptions.
- How well the workflow handles Nigerian Pidgin and local product names.
- How it should handle incomplete item information.
- Whether sellers actually use the WhatsApp send button rather than copying captions.
- Which clothing descriptions need the most correction.
Those questions matter because the tool’s value depends on more than generating fluent text: it must preserve product facts, work with the language sellers use, and fit their existing WhatsApp selling routine.
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