According to @godofprompt, a key trend in prompt engineering is Multi-Shot with Failure Cases, where AI engineers provide models with both good and bad examples, along with explicit explanations of why certain outputs fail. This technique establishes clearer output boundaries and improves model reliability for technical applications, such as explaining API rate limiting. By systematically demonstrating what not to do, businesses can reduce model hallucinations and ensure higher quality, more predictable outputs for enterprise AI deployments (source: @godofprompt, Dec 10, 2025). This approach is gaining traction among AI professionals seeking to deliver robust, production-ready generative AI solutions.

According to DeepLearning.AI, Microsoft and Nvidia are making significant investments of up to $10 billion and $5 billion respectively in Anthropic, a leading artificial intelligence company. This move will make Anthropic's Claude AI models available on all three major cloud platforms: Microsoft Azure, Google Cloud, and Amazon Web Services. As part of the agreement, Anthropic will purchase $30 billion in cloud computing capacity from Microsoft. The combined investments value Anthropic at $350 billion, nearly double its September valuation. This strategic partnership not only reinforces Anthropic's position as a key player in the generative AI market but also presents new business opportunities for enterprise adoption of Claude AI across diverse cloud ecosystems. By leveraging the infrastructure of all three major cloud providers, Anthropic aims to accelerate the deployment of large language models for businesses seeking scalable AI solutions (source: DeepLearning.AI).

According to DeepLearning.AI, Alibaba has launched Qwen3-Next-80B-A3B, an 80-billion-parameter mixture-of-experts AI model available in Base, Instruct, and Thinking variants under an open-weights Apache 2.0 license. Designed for faster long-context inference, the model replaces standard attention layers with Gated DeltaNet and gated attention mechanisms, enhancing efficiency in processing extended context windows. Trained on a 15 trillion token subset of the Qwen3 dataset and fine-tuned with GSPO, Qwen3-Next-80B-A3B enables multi-token prediction and accommodates input lengths up to 262,144 tokens, offering significant improvements for enterprise-level generative AI, document analysis, and large-scale conversational applications. (Source: DeepLearning.AI Twitter, 2025-09-22)