How to Choose the RIGHT Embedding Model | How Production Teams Evaluate Embedding Models

Most people choose embedding models using leaderboard scores. Production AI teams don’t. They evaluate retrieval behavior on real queries, hard negatives, chunking strategies, latency, BM25 baselines, rerankers, and domain understanding. In this video, I break down how embedding models are actually evaluated for RAG systems in production — including Recall@K, Precision@K, MRR, NDCG, benchmark creation, hard negatives, chunking effects, rerankers, and why many AI systems fail before production. Topics covered: How to build a retrieval benchmark Recall@K vs Precision@K MRR vs NDCG explained simply Hard negatives in retrieval Why chunking changes embedding quality BM25 vs vector search Production tradeoffs: latency, storage, ANN search Cross-encoder rerankers Real-world RAG evaluation strategy If you're building RAG systems, AI agents, semantic search, or production AI pipelines, this video will save you months of confusion. #RAG #AIEngineering #Embeddings #LLM #VectorDatabase #SemanticSearch #GenerativeAI #MachineLearning #AIAgents #RetrievalAugmentedGeneration