Agentic AI doesn’t have a model problem. It has a retrieval problem.
On BrowseComp-Plus, GPT-4.1 answered 93.49% of the questions correctly when it was given the supporting documents directly. When an agent The post A…
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Think you need a better reranker? Check your retrieval first.
You built a search system using the latest, greatest models, and maybe you were thrilled with the outcome. But what The post Think you need a better…
Want to scale AI agents without breaking anything? Retrieval engineering is the answer.
AI agents are multiplying as corporations adopt the technology in record numbers. Smarter underlying models, better tool use, and improved The post…
AI agents are making retrieval engineering a core engineering discipline
AI agents are changing retrieval requirements. As organizations move from chatbots to AI systems that investigate, reason, and act on The post AI ag…
Personalization is a ranking problem — architecture makes it work
Every product team is chasing the same moment: The user opens a page and thinks, this understands me. A shopper The post Personalization is a rankin…
Is retrieval engineering becoming AI’s next bottleneck?
Public AI assistants have become so commonplace that software vendors are increasingly adding AI search, conversational experiences, and AI agents T…
Why retrieval quality is becoming the defining challenge in AI agent architecture
Agentic systems usually have two jobs: Build context, then use that context to produce an answer or action. Many failures The post Why retrieval qua…
Your agent wants to search like a 2010 quant
AI agents need the right information to work well. Whether they manage to find it is the difference between success The post Your agent wants to sea…
Why AI retrieval and ranking need more than vector search
A recent GigaOm CxO Decision Brief explores how AI retrieval architectures are evolving beyond flat vector databases as organizations combine The po…
AI retrieval at scale is becoming a systems problem, not a tooling problem
AI retrieval has moved well beyond embeddings and vector search. Early retrieval architectures focused primarily on semantic similarity. Still, prod…
Cut your AI search costs without sacrificing quality
The cost that’s driving your AI search bill Every organization running AI-powered search faces the same hidden cost driver: query The post Cut your…
Why production RAG systems give confident, wrong answers at scale
In production RAG systems, the biggest bottleneck usually isn’t the LLM. It’s retrieval. Most teams start with a simple pattern: The pos…