Choosing between custom and off-the-shelf LLMs affects cost, security, and control. This framework helps CTOs match the right approach to their actual use case.

Without the ability to benchmark Large Language Models (LLMs), it is difficult for consumers and businesses to understand ...

If you've watched an AI presentation recently, you've probably heard a lot about "guardrails." It's a fitting analogy: Just like high-speed highways need steel barriers to prevent catastrophic crashes ...

A controlled study of four frontier chatbots building helmet-detection models finds that the most detailed prompts can ...

A new perspective in Discover Artificial Intelligence argues that prompt engineering is merely a subproblem within context engineering, a system-level discipline for orchestrating data, tools, memory ...

For conventional software, testing has traditionally centered on predefined requirements and expected outcomes. Generative AI ...

Microsoft Decision-1 scores AI routing decisions 35 times faster than GPT-6 Sol at $0.042 per million input tokens, with ...

Explore LLM architectures, general-purpose limitations, and why adapting models through Fine-Tuning and RAG is the real engineering challenge.

There are many practical benefits of implementing observability across AI workloads and LLM applications for engineering ...

Researchers at Viterbi School of Engineering studied artificial intelligence’s ability to reliably interpret the emotional ...

Bears say the capabilities of competing LLMs, including those from leading labs such as Anthropic and OpenAI, are already ...

Context Language Models (CLMs) let AI agents edit their own working context. Explore how they work, early benchmark results, ...