Large language models are remarkable text generators, but they have a well-documented weakness: they hallucinate. Without access to verified, up-to-date information, they confidently produce plausible-sounding answers that are factually wrong. Retrieval-Augmented Generation (RAG) emerged as the standard solution, grounding model outputs in external knowledge sources and dramatically reducing hallucinations across enterprise applications. But as organizations …

Agentic RAG vs. Naive RAG: When Your Retrieval System Should Think for Itself Read More »

Gemini 4 Argon is a step forward in what models can do — especially when it comes to deep work in complex, real-world tasks across domains including coding, finance, legal, cybersecurity, and science. Its cyberdefense capabilities are frontier-level and designed to give defenders an advantage over attackers. To maximize its potential, we first wanted cyber …

Developing Gemini 4 Argon responsibly and safely Read More »

Previous article: When All You Have Are Decoders, Every Decision Looks Like Generation ContentsTLDRWhy we put decision models to the testWhy runtime candidates change the problemOne decision contract, three question typesSeven open projects to tryHow we compared themKeeping the comparison consistentWhat happened in the two panelsCan the model choose among the same routes?What if the …

Jev Your Engines: Seven Open Decision Models vs. a Generative LLM Read More »

ContentsIntroductionZero-Shot vs. Few-Shot ClassificationRunning Few-Shot Classification with Scikit-LLM, Step by StepWrapping Up Introduction If you have worked with building text classification models, chances are you got familiar with the usual drill: collecting hundreds to thousands of labeled examples, cleaning the raw text, converting it to suitable numerical representations for machine learning models — like embeddings …

Few-Shot Text Classification with Scikit-LLM Read More »

Gemini 4 Argon marks a step change in physical understanding, code generation, and visual-semantic reasoning—unlocking new workflows across the robotics stack and accelerating progress toward physical AGI. You can see this leap in capability on the Embodied Reasoning (ERQA) benchmark, which tests a model’s ability to understand and solve physical, spatial problems in 3D environments. …

Gemini 4 Argon for Robotics Read More »