AI Terminologies Explained - Part 3

You can find part four of this video series here >> Part Four

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So far you’ve learned how AI thinks and how to have effective conversations with it. But up until now it has all been general, off-the-shelf AI. That’s like hiring a brilliant new employee who doesn’t yet know your brand voice, your products, or your processes. The real power comes when you customize it to become an expert on your business.
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🎯 Fine-tuning is the process of teaching an AI your company’s style and knowledge. You feed it examples like past marketing emails, customer support chats, or technical documents. The AI then learns your tone, your product details, and how you communicate, so when you ask it to write something it sounds like you.
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📊 Retrieval-Augmented Generation (RAG) keeps your AI up to date. Instead of retraining it every time your data changes, you connect it to your live database. When a customer asks about stock, it retrieves the latest information first, then answers with complete accuracy.
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📑 Context engineering is the skill of giving the AI the right information before it works. Think of it as writing a briefing document. For example, provide a customer’s purchase history before asking the AI to draft a support email. The better the context, the better the results.
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🛠️ Open-source models give you maximum flexibility. These are models where the underlying code is freely available. You can fine-tune them on your own servers, protect your data, and avoid being tied to one company’s platform.
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Put together: Fine-tuning gives the AI your brand voice, RAG connects it to real-time business data, context engineering ensures it has the right briefing, and open-source models give you flexibility and control. This is how you transform a general AI into a bespoke expert that works exactly the way your business needs.

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