Peter Smith
⬤ Large language models keep changing how developers work with AI systems. GPT-4's arrival marked a turning point, showing how well the model could express its capabilities when guided by structured pattern prompts. This experience directly shaped the development of pattern language frameworks for generative, reasoning, and agentic AI.
⬤ GPT-4's impact went beyond better text generation. With well-designed patterns, the model revealed deeper reasoning behaviors, helping users understand and use its internal capabilities more effectively. These interactions established new ways of communicating with large language models through intentional prompt structures instead of random instructions.
⬤ More recently, Claude Code has shown the ability to create novel epistemological methods. This marks another step forward in how advanced models handle sophisticated queries. The focus is moving from just improving model size or performance toward refining methods that extract and guide reasoning processes within these systems.
⬤ This progression points to a broader shift in AI, where advances increasingly depend on how models are queried rather than architecture alone. As query techniques get more refined, they're enabling deeper access to the latent cognitive potential of large language models. Future innovation may be driven by improved interaction design, unlocking new capabilities across generative, reasoning, and agent-based AI applications.
Peter Smith
