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This skill covers strategies for optimizing Large Language Model context windows to prevent context rot. It includes techniques like summarization, trimming, routing, and prioritization to manage token budgets effectively. Developers can use these methods to build multi-turn conversation systems where context length is a constraint, leveraging tools like tiktoken and LangChain for implementation.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

Expert in building Retrieval-Augmented Generation systems for LLM applications. Masters embedding models, vector databases, chunking strategies, and retrieval optimization. Capable of designing retrieval pipelines, implementing hybrid search, and optimizing context windows to ensure high-quality generation from retrieved documents.