GPT Style Models Tokenize Text for Training (With Code) (manthanguptaa.in)

A recent blog post delves into the intricacies of developing agentic systems—AI-driven platforms capable of performing complex tasks such as software development and data analysis. As these systems transition from prototypes to production environments, the author emphasizes the need for teams to balance three critical dimensions: cost, latency, and accuracy. Significant trade-offs often exist, where enhancements in accuracy can result in increased costs and delays, highlighting the importance of a structured decision-making framework when building these systems. The blog offers practical guidance on optimizing agentic systems through strategic techniques like the Planner-Executor architecture, which separates task planning from execution, and the use of Chain of Thought prompting to enhance reasoning and output quality. Additionally, it explores leveraging multiple parallel agents to improve accuracy through ensemble methods, and how maintaining a file system to track state can facilitate smoother workflows. This content is especially relevant for AI and ML practitioners looking to refine their applications, providing them with concrete metrics and actionable insights to bolster system performance while managing architectural constraints.