OpenAI Embeddings Explained: text-embedding-3 Small vs Large (with Code & Benchmarks)

In this video, we dive deep into OpenAI's powerful embedding models: text-embedding-3-small and text-embedding-3-large. You’ll learn: What embeddings are and why they matter in modern AI workflows How text-embedding-3-small compares to top models like e5-large, bge, and ada-002 Performance breakdowns using MTEB benchmarks Real-world use cases: Semantic Search, RAG, Recommendations, and more Hands-on examples in Python, JavaScript, and Go Best practices for production-ready systems (normalization, batching, indexing) 📊 Benchmark Comparison Table Included 🧠 Demos: Product Search, Chatbot Memory 💡 Bonus: Upgrade tips from ada-002 & optimization strategies for speed vs quality 👇 Get started now and see why text-embedding-3-small is the best bang for your buck! 🔗 Timestamps: 00:00 – Intro 01:02 – What are embeddings? 02:45 – Why text-embedding-3 is special 04:20 – MTEB Benchmark Breakdown 06:15 – Model Comparison Table 09:00 – Use Cases & Real-World Demos 11:40 – Python, JS & Go Code Examples 14:00 – Best Practices & Tips 16:20 – 3-Small vs 3-Large: Which one to choose? 18:00 – Final Thoughts 👍 Like, subscribe, and turn on notifications for more LLM and AI deep dives! #OpenAI #Embeddings #RAG #LLM #textEmbedding3 #SemanticSearch #AItools #Python #JavaScript #Go #Qdrant #vectorsearch https://www.cholakovit.com