According to @karpathy, building large language models (LLMs) like ChatGPT involves a systematic process that includes data collection, model architecture design, large-scale training, and deployment. Karpathy emphasizes starting with massive, high-quality text datasets for pretraining, leveraging transformer-based architectures, and employing distributed training on powerful GPU clusters to achieve state-of-the-art results (Source: @karpathy via X.com). For practical applications, he highlights the importance of fine-tuning on domain-specific data to enhance performance in targeted business use-cases such as customer support automation, code generation, and content creation. This step-by-step methodology offers substantial opportunities for organizations looking to develop proprietary AI solutions and differentiate in competitive markets (Source: @karpathy, 2024).

According to @andrewmayne, @markchen90, and @nickaturley on the OpenAI Podcast Episode 2, the discussion focused on the practical process behind ChatGPT's development, revealing how OpenAI approaches product design and iteration to ensure robust AI deployment (source: @andrewmayne via Twitter, June 2024). The episode also explored future AI business opportunities in agentic coding—where models autonomously write and optimize code—and in multimodal assistants that combine text, image, and audio capabilities, signaling strategic trends for enterprise adoption and next-generation AI services (source: OpenAI Podcast Episode 2).