Effective context engineering for AI agents
9 hours ago
- #AI
- #LLMs
- #ContextEngineering
- Context engineering is emerging as a new focus in AI, shifting from prompt engineering to optimizing the configuration of context for desired model behavior.
- Context refers to the tokens included when sampling from an LLM, and context engineering involves optimizing these tokens against LLM constraints.
- Prompt engineering focuses on writing effective prompts, while context engineering manages the entire context state, including system instructions, tools, and message history.
- LLMs experience context rot, where their ability to recall information decreases as the context window grows, necessitating careful curation of tokens.
- Effective context engineering involves finding the smallest set of high-signal tokens to maximize desired outcomes, balancing specificity and flexibility in system prompts.
- Tools for agents should be efficient, self-contained, and clear, avoiding bloated tool sets that lead to ambiguity.
- Examples (few-shot prompting) should be diverse and canonical, providing clear signals for expected behavior without overloading the prompt with edge cases.
- Context retrieval strategies are shifting from pre-inference retrieval to 'just-in-time' approaches, where agents dynamically load data at runtime.
- Long-horizon tasks require techniques like compaction (summarizing context), structured note-taking, and sub-agent architectures to manage context limitations.
- The guiding principle of context engineering is to maximize the utility of the model's limited attention budget by curating high-signal tokens.