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<rss version="2.0"><channel><title>CUTEADMOA Blog</title><link>https://cuteadmoa.site/blog/</link><description>Guides on building, fine-tuning and deploying your own AI models.</description><language>en</language><item><title>How to train your own AI model on your own data (a practical 2026 guide)</title><link>https://cuteadmoa.site/blog/how-to-train-your-own-ai-model-on-your-own-data.html</link><guid>https://cuteadmoa.site/blog/how-to-train-your-own-ai-model-on-your-own-data.html</guid><pubDate>2026-09-22T06:00:00+06:00</pubDate><description>A step-by-step guide to training a custom AI model on your own data: data readiness, fine-tuning vs. from-scratch, dataset preparation, evaluation, quantisation and deployment.</description></item><item><title>Fine-tuning vs. RAG: which should you choose for your AI assistant?</title><link>https://cuteadmoa.site/blog/fine-tuning-vs-rag-which-should-you-choose.html</link><guid>https://cuteadmoa.site/blog/fine-tuning-vs-rag-which-should-you-choose.html</guid><pubDate>2026-09-22T06:00:00+06:00</pubDate><description>A practical comparison of fine-tuning and retrieval-augmented generation: what each changes, when each wins, and how to combine them for a domain-specific assistant.</description></item><item><title>What does it cost to train a custom AI model? A realistic breakdown</title><link>https://cuteadmoa.site/blog/what-does-it-cost-to-train-a-custom-ai-model.html</link><guid>https://cuteadmoa.site/blog/what-does-it-cost-to-train-a-custom-ai-model.html</guid><pubDate>2026-09-22T06:00:00+06:00</pubDate><description>Where the money actually goes when you train a custom AI model — data preparation, compute, evaluation and running costs — and how to keep each one down.</description></item><item><title>Preparing a Dataset for Fine-Tuning: A Cleaning Checklist</title><link>https://cuteadmoa.site/blog/preparing-a-dataset-for-fine-tuning-a-cleaning-checklist.html</link><guid>https://cuteadmoa.site/blog/preparing-a-dataset-for-fine-tuning-a-cleaning-checklist.html</guid><pubDate>2026-09-22T06:00:00+06:00</pubDate><description>A practical cleaning checklist for fine-tuning datasets: deduplication, formatting, label checks, balanced splits and the review pass that catches errors.</description></item></channel></rss>