As LLMs evolve, I believe that while CoT remains simple and transparent, managing the growing complexity of prompts and multi-inference architectures will demand more sophisticated tools and a strong focus on data-centric approaches.
Human oversight will be essential to maintaining the integrity of these systems.
As LLM-based applications become more complex, their underlying processes must be accommodated somewhere, and preferably a resilient platform that can handle the growing functionality and complexity.
The prompt engineering process itself can become intricate, requiring dedicated infrastructure to manage data flow, API calls, and multi-step reasoning.
But as this complexity scales, introducing an agentic approach becomes essential to scale automated tasks, manage complex workflows, and navigate digital environments efficiently.
These agents enable applications to break down complex requests into manageable steps, optimising both performance and scalability.
Ultimately, hosting this complexity requires adaptable systems that support real-time interaction and seamless integration with broader data and AI ecosystems.
Strategic knowledge refers to a clear method or principle that guides reasoning toward a correct and stable solution. It involves using structured processes that logically lead to the desired outcome, thereby improving the stability and quality of CoT generation.
