AI BriefingThe Rise of Context Engineering
Key point
The key to improving LLM agent performance lies in context engineering, which dynamically provides the correct information and tools.
Details
Context engineering refers to building dynamic systems that provide LLMs with the right information and tools in the appropriate format to perform tasks.
Most agent failures occur because the model is not provided with the appropriate context, instructions, and tools. As LLM applications evolve from single prompts to complex agent systems, this is becoming the most critical skill for AI engineers.
Context engineering has the following characteristics:
- System: It collects context from various sources, including developers, users, previous interactions, and tool calls.
- Dynamism: Since context flows in dynamically, the logic for constructing the final prompt must also be dynamic.
- Information and Tools: Since LLMs cannot read minds, providing the correct information and tools to perform tasks is essential.
- Format: Just as with human communication, the manner (format) of communicating with LLMs is important.
LangGraph and LangSmith provide agent orchestration and monitoring/evaluation capabilities, respectively, to support this context engineering.
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