Everything about context-engineering
7 connected nodes across dictionary, workflows, comparisons, prompts, tool stacks and use cases.
Prompt Caching
Reusing the processed form of a repeated prompt prefix so later requests are cheaper and faster.
Contextual Compression
Contextual compression is a technique used to reduce the size of the input context for a Large Language Model (LLM) while retaining its most relevant information, typically by summarizing or filtering.
Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
Dynamic Context Insertion Workflow
This workflow details how to dynamically inject context-specific information into LLM prompts based on user queries or application state, improving response accuracy and relevance.
Multi-Turn Context Management Workflow
This workflow manages conversation history and other dynamic context in multi-turn interactions with LLMs, ensuring coherence and relevance over extended dialogues.
LLM Context Management Stack
A technology stack for effectively managing and optimizing the context provided to large language models, ensuring efficient, relevant, and cost-effective operations.
RAG Context Enrichment Stack
A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.