venturebeat
The teacher is the new engineer: Inside the rise of AI enablement and PromptOps

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 [...]

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venturebeat
Microsoft's new AI training method eliminates bloated system prompts without sacrificing model performance

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 [...]

Match Score: 113.83

venturebeat
Gong launches ‘Mission Andromeda’ with AI sales coaching, chatbot and open MCP connections to rivals

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 [...]

Match Score: 94.02

venturebeat
Why LinkedIn says prompting was a non-starter — and small models was the breakthrough

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 [...]

Match Score: 57.73

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Vibe coding with overeager AI: Lessons learned from treating Google AI Studio like a teammate

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 [...]

Match Score: 52.20

venturebeat
MIT's new fine-tuning method lets LLMs learn new skills without losing old ones

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 [...]

Match Score: 44.37

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Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

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 [...]

Match Score: 44.23

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Anthropic rolls out Code Review for Claude Code as it sues over Pentagon blacklist and partners with Microsoft

Anthropic on Monday released Code Review, a multi-agent code review system built into Claude Code that dispatches teams of AI agents to scrutinize every pull request for bugs that human reviewers rout [...]

Match Score: 36.89

venturebeat
OpenAI upgrades ChatGPT with interactive learning tools as lawsuits and Pentagon backlash mount

OpenAI on Monday launched a set of interactive visual tools inside ChatGPT that let users manipulate mathematical and scientific formulas in real time — a genuinely impressive education feature that [...]

Match Score: 35.27

venturebeat
How to avoid becoming an “AI-first” company with zero real AI usage

Remember the first time you heard your company was going AI-first?Maybe it came through an all-hands that felt different from the others. The CEO said, “By Q3, every team should have integrated AI i [...]

Match Score: 33.05