A new Google DeepMind study reveals a fundamental bottleneck in single-vector embeddings, explaining why even the most advanced RAG systems can fail unexpectedly. The post New DeepMind research reveals a fundamental limit in vector embeddings for RAG applications first appeared on TechTalks.| TechTalks
This compact embedding model is a key piece in a larger strategy of small language models, favoring a fleet of efficient specialists models over one large LLM. The post How Google’s EmbeddingGemma can unlock new edge AI applications first appeared on TechTalks.| TechTalks