Context Engineering is a crucial aspect of enhancing AI performance, and Inngest demonstrates its application by building an AI Research Assistant that answers domain-specific questions effectively using a sophisticated context pipeline. The process involves selecting high-quality data sources such as ArXiv, GitHub, and web searches, and leveraging Inngest workflows to retrieve, parallelize, and transform data into relevant contexts. The AI Research Assistant employs the "Orchestrator-workers" pattern, where the context is divided among specialized models—like GPT-4 and Claude—to compress and augment information, ensuring accurate and unbiased responses. This approach highlights the importance of orchestration, data source quality, and the balancing act of rapid data retrieval against API rate limits, ultimately refining the context to optimize the reasoning capabilities of large language models (LLMs). The open-source nature of the project encourages further exploration and experimentation by the community.