A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and LLMs. Helps operations teams optimize AI agent performance and accuracy by improving context handling.
git clone https://github.com/yzfly/awesome-context-engineering.gitA curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and LLMs. Helps operations teams optimize AI agent performance and accuracy by improving context handling.
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Act as an expert in Context Engineering for AI agents and LLMs. Provide a curated list of resources, papers, tools, and best practices for [COMPANY] in the [INDUSTRY] sector. Focus on [DATA] and how it can be leveraged to improve AI performance.
# Context Engineering Resources for [COMPANY] in the [INDUSTRY] Sector ## Key Papers - "Attention Is All You Need" by Vaswani et al. (2017) - Foundational paper on transformer models and context handling. - "Longformer: The Long-Document Transformer" by Beltagy et al. (2020) - Addresses long-range context in documents. ## Tools and Frameworks - **LangChain**: A framework for developing applications powered by language models. Supports context management and memory. - **LlamaIndex**: A data framework for your LLM applications. Provides tools for context window management and retrieval. ## Best Practices - **Chunking Strategies**: Implement sliding window or overlapping window techniques for effective context management. - **Context Pruning**: Remove irrelevant information from the context to improve performance. - **Context Augmentation**: Use retrieval-augmented generation (RAG) to fetch relevant information dynamically.
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