Drawing on Latent Space’s interview with Shunyu Yao, this article reviews ReAct, Reflexion, Tree of Thoughts, memory, benchmarks, ACI, and Agent UX, and summarizes the importance of tools, environments, evaluation, and interface design when putting AI agents into practice.
Using the bandwidth gap between hidden states and tokens, this article offers one explanation for LLM engineering phenomena such as Chain of Thought, prompt length, few-shot learning, personality drift, and hallucinations.
A long-form Prompt Engineering guide for AI application engineers, covering foundational principles, context design, task chains, injection defenses, agent prompt design, and evaluation-driven development.
Understand what LLM Chain-of-Thought (CoT) is and how prompt engineering can elicit Chain-of-Thought (CoT) from an LLM.