Tag: Semantic Search
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Beyond Simple RAG: How Context Engineering and GraphRAG Fix AI Performance
As Martin Keen from IBM explains, context is the single biggest bottleneck in getting AI to do what you want. While simple semantic search architectures helped us get started, building truly reliable, enterprise-grade AI systems requires shifting toward Context Engineering, Retrieval-Augmented Generation (RAG), and advanced GraphRAG models.
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Gemini Embedding 2: One Model to Index Them All
Imagine building a search system that can handle text, images, audio recordings, video clips, and PDFs—all within the same search query. Traditionally, this would require a complex pipeline: multiple vector stores, various specialized embedding models (like CLIP for images or Whisper for audio transcription), and a messy fusion layer to combine the results. [01:32] With…