Context Engineering - What it is, and techniques to consider

Context engineering is an emerging field in AI that emphasizes the importance of curating the right context for language models (LLMs) to perform tasks effectively, distinguishing it from prompt engineering, which focuses on providing specific instructions. This discipline involves filling the context window with relevant information, taking into account limitations such as the context window's size. Context engineering requires careful selection and organization of various elements, such as system prompts, user inputs, short and long-term memory, knowledge base retrievals, and structured outputs. Tools like LlamaIndex and LlamaCloud offer solutions for implementing these principles, enabling the creation of agentic systems that optimize context usage while avoiding overload. Workflow engineering complements this by outlining sequences of LLM calls and tool interactions, ensuring efficient task completion and context management. These strategies are crucial for developing robust AI applications that can handle complex tasks by leveraging both immediate context and broader workflow planning.

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