[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 74K followers Created: 2025-07-18 23:21:51 UTC Beautiful Survey paper on Context Engineering on 1400 research papers. XXX pages of comprehensive taxonomy decomposing Context Engineering into its foundational Components and the sophisticated Implementations. LLMs stumble when the prompt is messy, so this survey maps every tool for cleaning, stretching, and storing context. The authors show that smart context handling, not just bigger models, drives more accurate and reliable answers. 🗺️ Why define “context engineering” at all? Today, prompt tricks, retrieval add-ons, long-attention tweaks, and memory hacks grow in separate silos. That split hides how they all chase one goal: feed the model the right bytes at the right moment. So context engineering captures the full pipeline that creates, processes, and manages those bytes, then lines it up in one taxonomy. 🧩 Three foundational building blocks X. Context Generation & Retrieval covers everything from Chain-of-Thought templates to RAG assemblies that pull fresh facts. X. Context Processing tackles long sequence tricks like FlashAttention and Mamba so the model can scan 1M-token logs without choking. X. Context Management stores or compresses old exchanges with methods such as Hierarchical Memory or KV Cache pruning to fit future calls.  XXXXX engagements  **Related Topics** [cleaning](/topic/cleaning) [context engineering](/topic/context-engineering) [Post Link](https://x.com/rohanpaul_ai/status/1946349730621202762)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Rohan Paul @rohanpaul_ai on x 74K followers
Created: 2025-07-18 23:21:51 UTC
Beautiful Survey paper on Context Engineering on 1400 research papers.
XXX pages of comprehensive taxonomy decomposing Context Engineering into its foundational Components and the sophisticated Implementations.
LLMs stumble when the prompt is messy, so this survey maps every tool for cleaning, stretching, and storing context.
The authors show that smart context handling, not just bigger models, drives more accurate and reliable answers.
🗺️ Why define “context engineering” at all?
Today, prompt tricks, retrieval add-ons, long-attention tweaks, and memory hacks grow in separate silos. That split hides how they all chase one goal: feed the model the right bytes at the right moment.
So context engineering captures the full pipeline that creates, processes, and manages those bytes, then lines it up in one taxonomy.
🧩 Three foundational building blocks
X. Context Generation & Retrieval covers everything from Chain-of-Thought templates to RAG assemblies that pull fresh facts.
X. Context Processing tackles long sequence tricks like FlashAttention and Mamba so the model can scan 1M-token logs without choking.
X. Context Management stores or compresses old exchanges with methods such as Hierarchical Memory or KV Cache pruning to fit future calls.
XXXXX engagements
Related Topics cleaning context engineering