Retrieval, RAG & Search

What Happened Production teams running retrieval-augmented generation (RAG) and LLM agents found that starting agents without domain-specific context drives exploratory tool calls, higher latency, token cost and hallucinations. The practical fix is to assemble a structured, precomputed context layer from catalog, policy and session signals and provide it to the model before the agent’s first…

Executive summary Short: A 2026 benchmark on a 5,000-item apparel & footwear catalog found that a simple average of image and text embeddings (L2-normalize each, mean, then L2-normalize) produced substantially better product search results than image-only or text-only retrieval—up to ~1.5× improvement on top-of-list metrics (Recall@1/5/10, MRR, nDCG@10). The study also showed multimodal averaging improved…