According to God of Prompt on Twitter, current prompting methods for large language models (LLMs) face significant issues with hallucination, where models confidently produce incorrect information (source: @godofprompt, Jan 5, 2026). While few-shot prompting can partially mitigate this by providing examples, it is limited by the quality of chosen examples, token budget restrictions, and does not fully eliminate hallucinations. These persistent challenges highlight the need for more robust AI model architectures and advanced prompt engineering to ensure reliable outputs for enterprise and consumer applications.

According to DeepLearning.AI on Twitter, their Retrieval Augmented Generation course offers a comprehensive overview of how large language models (LLMs) generate tokens, the root causes of model hallucinations, and the factuality improvements achieved through retrieval-based grounding. The course also analyzes practical tradeoffs such as prompt length, compute costs, and context window limitations, using Together AI’s production-ready tools as case studies. This curriculum addresses real-world enterprise needs for accurate, cost-effective generative AI, providing valuable insights for businesses seeking to deploy advanced retrieval-augmented solutions and optimize AI-driven workflows (source: DeepLearning.AI Twitter, August 28, 2025).