Marcus Rossi

Models fabricate answers on recent topics unless systems actively retrieve live information.

Stale citations in AI answers pose real risks without explicit freshness signals.

Search APIs optimize the wrong metrics, leaving citations factually unsupported.

Understanding what search benchmarks actually measure matters more than chasing the highest scores.

Bing's retirement forces teams to choose between speed and accuracy in search APIs.

Building production-grade research automation requires three separate layers, not one tool purchase.

Semantic search finds related ideas even when documents use different words to express them.

Hybrid retrieval outperforms pure vector or keyword search for real-world RAG systems.

Errors in early pipeline stages propagate invisibly through to final answers.

Successful agentic AI depends on architecture choices, not model selection alone.

Tool descriptions written for humans, not LLMs, cause more routing failures than any algorithm.

Allocate latency to every pipeline stage, not just the LLM.

Understanding rate-limit algorithms prevents retry logic from making outages worse.

Agents stumble when knowledge is stale, not when reasoning fails.

Live web data replaces hallucination with auditable facts.

Smaller, focused contexts produce more reliable outputs than enormous ones.

General-purpose scrapers fail AI pipelines by delivering boilerplate alongside content.