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Decoding Data Science

146 members • Free

99 contributions to Decoding Data Science
Most RAG systems don’t fail at generation. They fail at retrieval.
If the correct information is poorly chunked or ranked too low, even the best LLM cannot recover it. Focus first on: • Structure-aware chunking • Retrieval quality and recall • Testing the right retrieval depth • Reducing noise and “lost in the middle” failures Better retrieval improves accuracy, latency and cost. RAG is a retrieval problem first—and a generation problem second.
Most RAG systems don’t fail at generation. They fail at retrieval.
The AI model race is changing. The next battle may be economics, not just intelligence.
As open-weight models close the capability gap, the conversation shifts from “Which model is smartest?” to: → What does inference actually cost at scale? → When should workloads be dynamically routed? → How important will sovereign AI infrastructure become? For AI builders and leaders, architecture + economics + deployment strategy are becoming as important as model choice. The frontier is no longer just the model. It is the system around it. What do you think will matter most: model capability, cost, or sovereignty?
The AI model race is changing. The next battle may be economics, not just intelligence.
Most AI agents work in demos. Far fewer survive production.
Production-grade Agentic AI requires more than adding multiple agents. It needs: • Strategic model routing • Strict tool contracts and policy gates • Trace-level evaluation • Human approval for high-risk actions The goal is not more agents—it is reliable, secure and measurable outcomes. What is the biggest challenge you face when moving AI agents from demo to deployment?
Most AI agents work in demos. Far fewer survive production.
Most RAG systems don’t fail at generation. They fail at retrieval.
If the correct information is poorly chunked or ranked too low, even the best LLM cannot recover it. Focus first on: • Structure-aware chunking • Retrieval quality and recall • Testing the right retrieval depth • Reducing noise and “lost in the middle” failures Better retrieval improves accuracy, latency and cost. RAG is a retrieval problem first—and a generation problem second.
Most RAG systems don’t fail at generation. They fail at retrieval.
The AI model race is changing. The next battle may be economics, not just intelligence.
As open-weight models close the capability gap, the conversation shifts from “Which model is smartest?” to: → What does inference actually cost at scale? → When should workloads be dynamically routed? → How important will sovereign AI infrastructure become? For AI builders and leaders, architecture + economics + deployment strategy are becoming as important as model choice. The frontier is no longer just the model. It is the system around it. What do you think will matter most: model capability, cost, or sovereignty?
The AI model race is changing. The next battle may be economics, not just intelligence.
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Mary Rose Delos Santos
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@mary-rose-delos-santos-2451
Heyy

Active 1d ago
Joined Apr 2, 2026