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  • React
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NEW

ZAYA1-8B: A Small-Parameter Model That Outperforms Big Competitors

The AI industry is shifting from the "bigger is better" era to a focus on intelligence per parameter. Companies are prioritizing models that deliver high performance with fewer resources. For example, ZAYA1-8B’s 760 million active parameters (out of 8.4 billion total) match or exceed results from…
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NEW

Why Static RAG Is Obsolete and Agents Are Rising

Watch: Agentic RAG vs RAGs by Rakesh Gohel Static RAG is obsolete because its rigid, two-stage design cannot adapt to the dynamic, multi-step reasoning demands of modern AI workflows.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…

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NEW

Why You Shouldn't Dump Project Rules into LLM Context

Watch: What is a Context Window? enable LLM Secrets by IBM Technology Project rules in LLM contexts matter because they directly impact efficiency, cost, and reliability in AI-assisted workflows. When developers "dump" project rules into LLM context-such as pasting entire style guides or…
NEW

Using Agents to Convert PDFs into Structured Data

Watch: Extracting Structured Data From PDFs | Full Python AI project for beginners (ft Docker) by Thu Vu PDF conversion matters because unstructured data in formats like PDFs creates significant operational inefficiencies and financial risks for businesses. Industry research shows that parsing a…
NEW

Using LLMs to Judge Their Own Outputs

LLM self-evaluation is critical for ensuring the reliability, fairness, and effectiveness of AI systems. When models judge their own outputs, they risk introducing biases that distort performance metrics, compromise decision-making, and erode trust. Research shows that even advanced models like…
Thumbnail Image of Tutorial Using LLMs to Judge Their Own Outputs