Tutorials on Agentic Rag

Watch: Agentic RAG vs RAGs by Rakesh Gohel RAG technology was replaced in healthcare due to critical limitations that undermined its reliability, safety, and scalability in clinical settings. While RAG systems initially promised to bridge knowledge gaps by grounding AI responses in curated data, healthcare clients like Schmitt-Thompson Clinical Content (STCC) and NHS South Yorkshire discovered systemic flaws that made them unsuitable for high-stakes applications. Below is a detailed breakdown of the challenges that led to its replacement.. In healthcare, hallucinations-fabricated or incorrect information generated by AI-pose life-threatening risks. STCC’s clinical triage guidelines, used by over 400 health systems, revealed that traditional RAG systems misinterpreted logic-based decision trees as natural language, leading to unsafe recommendations. For example, in 329 validated scenarios, 13 out of 16 guidelines fell below expert benchmarks, with errors in complex cases like Neurologic Deficit (85% accuracy vs. 96% benchmark). These inaccuracies stemmed from RAG’s inability to parse structured clinical logic, resulting in responses that prioritized fluency over factual correctness.

Watch: Agentic RAG vs RAGs by Rakesh Gohel Static RAG is obsolete because its rigid, two-stage design cannot adapt to the dynamic, multi-step reasoning demands of modern AI workflows. Traditional systems retrieve documents once and generate answers based on fixed context, making them brittle when queries require iterative refinement or cross-source synthesis. Industry data reveals that 57% of organizations now deploy agentic systems for complex tasks, while Static RAG pipelines struggle to scale beyond simple Q&A. This shift is driven by real-world failures: Static RAG produces hallucinations at rates of 12–14% in clinical scenarios and faltters on multi-hop reasoning, achieving only 34% accuracy on benchmarks like HotpotQA compared to agentic systems’ 89% , as detailed in the Real-World Applications and Case Studies section. Static RAG’s core flaw lies in its inability to address three critical failure modes: