How to Context Engineer to Optimize Question Answering Pipelines

Context engineering is presented as a critical method for optimizing question-answering pipelines, aiming to improve upon traditional Retrieval-Augmented Generation (RAG). The primary goals are to enhance output quality, lower operational costs, and increase speed by carefully managing the information fed to a Large Language Model. Techniques for improvement focus on two main areas: reducing irrelevant tokens through reranking or summarization, and adding more relevant documents by using better embedding models or expanding the search scope. The piece also introduces a more advanced agentic search approach, where an orchestrator agent coordinates sub-agents to dynamically find and process relevant information for a given query.