📘 Introduction

Artificial intelligence (AI) has rapidly evolved, and one of its most impactful applications lies in natural language processing (NLP). From search engines to conversational assistants, AI models power many of the tools we use daily. However, not all AI models are created for the same purpose. Two important categories—embedding models and chat models—are often confused, yet they serve very different roles in the AI ecosystem. In this post, we’ll break down what each of these models does, how they differ, and when you should use one over the other.

🧩 What Are Embedding Models?

Embedding models are designed to convert text (or other data types, such as images or audio) into numerical representations called vectors. These vectors capture the semantic meaning of text, allowing computers to understand similarity, relationships, and context between words, sentences, or documents.

🎯 Key Purpose

Generating meaningful text vectors for comparison and retrieval.

💡 Common Use Cases

    • 🔎 Semantic search (finding relevant documents or passages)
    • 🗂 Text classification
    • 🤝 Clustering similar content
    • 🛒 Recommendation systems

👉 Example

If you ask “What is AI?”, the model won’t generate an answer. Instead, it transforms the query and candidate answers into vectors:

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