This article systematically explains context-compression methods for production AI agents, distinguishing message trimming, tool-result cleanup, artifact externalization, structured summaries, and native compaction. Using Hermes Agent, LangChain v1, and LangGraph v1, it presents a layered implementation and evaluation approach.
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.
This article connects cross-entropy, perplexity, temperature, conditional entropy, hallucination detection, and prompt constraints into one line of reasoning, showing how entropy provides a unified language for understanding LLM training, inference, and product design. It also explains entropy’s practical value in RAG and engineering governance.
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.
Prompt engineering? It may not be as simple as you think.
Understand LLM prompt injection and several fundamental defensive measures.