Context engineering is fast becoming the backbone of serious AI deployments, especially those involving large language models (LLMs). Context engineering is the deliberate design, structuring, and management of the information ecosystem surrounding an AI model. Think of it as crafting not just the question, but the entire briefing memo, mood board, data warehouse, and toolkit that help an LLM give a decent answer. If you’re building a trading bot, customer service assistant, or research analyst powered by an LLM, you don’t want it guessing in the dark. Context engineering ensures it walks into the room prepped, briefed, and ready to speak intelligently about your client’s portfolio, market trends in sub-Saharan Africa, or whatever it might be. According to LlamaIndex, success in enterprise AI depends less on tweaking prompts and more on designing context pipelines that can integrate domain-specific knowledge, user preferences, compliance requirements, and temporal awareness. Finance is a perfect example: In financial analysis, client-facing chatbots, portfolio recommendations, context is key. With smart context pipelines, the LLM knows whether it’s speaking to a junior retail trader or a seasoned institutional player and deliver the information in the appropriate manner. As LangChain’s engineers put it, prompt engineering is fine for demos—but context engineering is what gets deployed in production. And production is where the money is. It involves integrating semantic search engines, versioned memory banks, and modular knowledge sources so the model doesn’t hallucinate a balance sheet or invent nonexistent market indices.