🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLMs and AI agents.

Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

A curated list of awesome open-source libraries for context engineering (Long-term memory, MCP: Model Context Protocol, Prompt/RAG Compression, Multi-Agent)

A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).

A curated list of resources for making AI agents truly understand you through Context Engineering.

A curated list of Context Engineering frameworks, tools, and best practices for building production-grade AI systems. Features BMAD-METHOD, AGENTS.md, Claude Code Spec Workflow, and Kiro Specs.

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🤖 Explore essential AI engineering resources, from research papers to tools, to help software engineers transition into AI roles effectively.

A list of resources about Context Engineering, including articles, papers and videos.

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Unlock the full potential of large language models by engineering smarter, more effective contexts. From retrieval techniques to memory architectures, from few-shot tricks to advanced chaining — this repo brings it all together.

Curated list of context engineering resources, tools, techniques, and best practices for AI/LLM systems

AI Tools for Enterprises

Traditional sentiment analysis often fails on social media because it relies on "Literal Keywords." For example, a basic model sees the word "sick" and flags it as Negative, even if the user meant "This is awesome!" in a slang context. This project implements Context Engineering to move from simple "Sentiment" to true Semantic Intent.