context-engineering

Optimize AI context windows by applying four strategies: Write, Select, Compress, and Isolate. Structuring input and managing information flow ensures the LLM understands your intent and produces high-quality, relevant outputs consistently.

The best way to structure system prompts is using clear techniques like XML tags and specific instructions. Prompt design foundations help craft effective prompts that improve an AI agent's ability to follow complex instructions accurately.

Manage agent memory patterns by integrating context management strategies into your workflow. This involves structuring system prompts and dynamically retrieving only the most relevant code snippets and documentation for the LLM.

Yes, you can integrate context engineering into CI pipelines. This integration automates context management, ensuring that system prompts and information flow are consistently optimized across different development stages for reliable AI outputs.

Prompt engineering focuses on designing effective prompts, while context engineering encompasses the broader optimization of the full context window. Context engineering uses strategies like Select and Compress to manage the entire information environment.

AI models often ignore instructions when long context inputs are unstructured. Applying context optimization strategies like Compress and Isolate manages the information environment, ensuring the model processes relevant code snippets and documentation effectively.