Enterprise GenAI (generative AI) deployments succeed when teams run them with the same discipline they apply to other user-facing services. The model sits in the middle of a pipeline that handles identity, policy, retrieval, inference, and logging. Each stage affects quality, latency, cost, and risk. A pilot can hide these dependencies. Production traffic exposes them….
The novel power of today’s AI is in its ability to deal with intent. This is a superpower, no doubt, but it creates a huge imperative for app developers: the need to map between the anything-is-possible large language model (LLM) and the strict capabilities of code. Unrestrained, LLM endpoints will let your user create unicorns and…
AI agents look brilliant in a demo because demos are friendly worlds. The data is curated, the tools behave, and nothing important changes while the agent is in mid-thought. Production is the opposite: data arrives late, facts conflict, permissions bite, APIs time out, and the underlying state changes constantly. That gap is why early “agents…
In the beginning, everyone was surprised that large language models (LLMs) could speak in words. Those days are long past, and now the focus is on the depth of knowledge. The best way to deliver this is with specialization. Instead of developing a one-size-fits-all leviathan, the best teams are building specialized models for niches—one for…
While generative AI has shown promising results in advancing software engineering, its inclusion within end-user applications is a different story. Features labeled as AI continue to pop up across every UI, but they’re not always helpful or useful. Often driven by hype, they can become a distraction, or worse, a productivity killer. “Many fall into…
Three key advantages of SLMs Division of labor: Modern AI architecture uses routers to send routine tasks to 7B-parameter SLMs, reserving trillion-parameter LLMs only for complex reasoning. Economic efficiency: For high-volume, repetitive tasks, SLMs can reduce cloud inference costs by up to 90% while providing near-instant latency. Privacy at the edge: Because SLMs can run…
Google’s Gemma 4 comes touted as the latest evolution of Google’s multi-modal model offerings. Gemma 4 not only offers reasoning and tool use, but vision and audio functionality, and it’s available in a range of model sizes that target servers and local devices. What’s striking about Gemma 4 is that even at the higher end…
Car buyers kick tires. Horse traders inspect the teeth. What should shoppers for large language models (LLMs) do? Here are 27 prescient questions that developers are asking before they adopt a particular model. Model capabilities are diverse, and not every application requires the same support. These questions will help you identify the best models for…
The AI agent market is projected to grow from $5.1 billion in 2024 to over $47 billion by 2030, yet Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027. The reason is not model capability. It is trust. Traditional AI evaluation tells you whether a model…
Everyone is building “agents,” but there is a large disagreement on what that means. David Loker, VP of AI at CodeRabbit, which runs one of the most widely deployed agentic code review systems in production, has a practical definition that cuts through the hype: The company’s code review “happens in a workflow” with “two agentic…
