Context engineering: Best practices for an emerging discipline

Context engineering is emerging as a vital discipline in AI-based development, focusing on providing large language models (LLMs) with the right context to enhance reliability and performance, surpassing the limitations of prompt engineering. Unlike prompt engineering, which often involves simple language tweaks, context engineering systematically selects, structures, and delivers comprehensive context including instructions, tool calls, and knowledge, treating it as an infrastructure rather than a mere background. This approach addresses the variability in performance due to model changes and the brittleness of prompt-only workflows, and it incorporates memory, retrieval, and context compaction strategies. Leaders like Tobi Lutke and Andrej Karpathy advocate for context engineering as it combines the art of intuition with the science of detailed task descriptions and example-based learning. The discipline requires a balance between offering too much or too little context to avoid errors, hallucinations, and inefficiencies, employing strategies like Retrieval-Augmented Generation (RAG) and context pruning to refine input quality. Technologies such as Redis provide a robust infrastructure for context management by offering fast, scalable, in-memory storage and vector search capabilities, enabling both short-term and long-term memory functions critical for developing agent-driven applications. As AI evolves, context engineering is poised to become an essential framework for professionalizing and scaling AI applications, ensuring that LLMs operate efficiently and effectively in complex, production-level environments.