Fix Agent Failures With Context Engineering for LLMs

Context engineering improves AI model performance when transitioning from trials to production by focusing on managing the data fed into the model with each request, rather than solely optimizing prompts. Engineers must oversee the entire context lifecycle, including managing retrieved data and previous interactions. Unlike prompt engineering, which emphasizes text formatting and instruction writing, context engineering dynamically aggregates data during each model request. This approach necessitates the design of automated systems for data collection and information filtering, enhancing effectiveness in production environments.