Uploaded September 2025 | Updated September 2026, 3 weeks ago
What practical ways can we use to enrich and modernize traditional RAG pipelines by moving beyond text-only chunk retrieval. The conversation focused on multimodal embeddings; converting images, audio, and video into vectors and storing them alongside text in a unified vector database, so queries can return mixed-media context (text + images + video) when appropriate. We also discussed the operational considerations for adopting multimodal RAG: evaluating retrieval accuracy thresholds before surfacing media to users, picking the right embedding models for your domain, and integrating multimodal retrieval into existing relevance and guardrail layers.
#RAG #RetrievalAugmentedGeneration #MultimodalAI #Embeddings #VectorSearch #AmazonTitan #LLM #GenerativeAI #AIResearch #MLOps #PromptEngineering #AITrends2025
What practical ways can we use to enrich and modernize traditional RAG pipelines by moving beyond text-only chunk retrieval. The conversation focused on multimodal embeddings; converting images, audio, and video into vectors and storing them alongside text in a unified vector database, so queries can return mixed-media context (text + images + video) when appropriate. We also discussed the operational considerations for adopting multimodal RAG: evaluating retrieval accuracy thresholds before surfacing media to users, picking the right embedding models for your domain, and integrating multimodal retrieval into existing relevance and guardrail layers.
#RAG #RetrievalAugmentedGeneration #MultimodalAI #Embeddings #VectorSearch #AmazonTitan #LLM #GenerativeAI #AIResearch #MLOps #PromptEngineering #AITrends2025
