As more companies quickly begin using gen AI, it’s important to avoid a big mistake that could impact its effectiveness: Proper onboarding. Companies spend time and money training new human workers to succeed, but when they use large language model (LLM) helpers, many treat them like simple tools that need no explanation. This isn't just a waste of resources; it's risky. Research shows that AI has advanced quickly from testing to actual use in 2024 to 2025, with almost a third of companies reporting a sharp increase in usage and acceptance from the previous year.Probabilistic systems need governance, not wishful thinkingUnlike traditional software, gen AI is probabilistic and adaptive. It learns from interaction, can drift as data or usage changes and operates in the gray zone [...]
Training AI reasoning models demands resources that most enterprise teams do not have. Engineering teams are often forced to choose between distilling knowledge from large, expensive models or relying [...]
In building LLM applications, enterprises often have to create very long system prompts to adjust the model’s behavior for their applications. These prompts contain company knowledge, preferences, a [...]
Gong, the revenue intelligence company that has spent a decade turning recorded sales calls into data, today launched what it calls Mission Andromeda — its most ambitious platform release to date, b [...]
Anthropic recently told its growth team to hire more product managers, not fewer. The reason, as reported in industry coverage, was that Claude Code had quietly turned its engineering org into a team [...]
LinkedIn is a leader in AI recommender systems, having developed them over the last 15-plus years. But getting to a next-gen recommendation stack for the job-seekers of tomorrow required a whole new [...]
Most discussions about vibe coding usually position generative AI as a backup singer rather than the frontman: Helpful as a performer to jump-start ideas, sketch early code structures and explore new [...]
As agentic AI workflows multiply the cost and latency of long reasoning chains, a team from the University of Maryland, Lawrence Livermore National Labs, Columbia University and TogetherAI has found a [...]
When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill.Researchers at MIT, the Improbab [...]