davidkimai/Context-Engineering

Features

• Modular Cognitive Toolsets - Decomposes complex logical problems into a sequence of specialized prompt templates.
• Conversation State Management - Implements a system for maintaining persistent memory across interaction turns by compressing history into internal state cells.
• Prompt Engineering - Provides a comprehensive framework for designing structured prompts to optimize LLM reasoning and accuracy.
• Agentic Workflow Orchestration - Orchestrates complex logical tasks by decomposing them into modular steps and multi-agent reasoning flows.
• Agentic Reasoning Frameworks - Employs architectural patterns to coordinate reasoning flows across multiple specialized agent templates.
• Cognitive Frameworks - Implements a cognitive architecture using modular reasoning structures to decompose complex problems.
• Context Augmentation Tools - Injects external documents into the prompt window to ground responses and reduce hallucinations.
• Context Window Optimizations - Ships techniques for reducing token overhead to maximize the available information density within the context window.
• State-Cell Memory Compression - Persists conversation history by condensing interaction data into compact internal state structures.
• Retrieval-Augmented Generation - Provides strategies for injecting external documents and factual grounding into prompts to reduce hallucinations.
• Conversational State Managers - Provides methods for maintaining conversation state and persisting memory through compact internal state cells.
• External Knowledge Integrators - Integrates factual documents into the prompt to ensure accurate and grounded responses.
• Functional Prompt Compositions - Provides a formal methodology for constructing complex instructions using programming patterns.
• Retrieval Augmented Generation - Dynamically inserts external documents into the active prompt window for factual grounding.
• State-Cell Compression - Persists conversation history by condensing interaction data into compact internal state structures.
• Prompt Engineering Frameworks - Provides a structured set of patterns and methodologies for optimizing LLM reasoning and memory via prompt engineering.
• Prompt Templates - Treats prompt construction as a formal programming task using structured reusable templates.
• Reasoning Orchestrators - Provides a framework for managing multi-step planning and chain-of-thought reasoning through specialized prompt templates.
• Semantic Field Mapping - Implements semantic field mapping to create stable conceptual structures within the model context.
• Token Optimizers - Maximizes information density per token by pruning noise to reduce inference latency.
• AI-Driven Decomposers - Breaks complicated problems into manageable steps using multi-agent flows and LLM-driven decomposition.
• Prompt Composition Patterns - Constructs prompts using formal programming patterns such as functional and object-oriented structures.
• Chain of Thought Implementations - Provides chain-of-thought frameworks and cognitive tools to increase accuracy in complex problem solving.
• RAG-Specific Context Optimizers - Prunes and manages retrieved documents injected into the context window to reduce hallucinations and token costs.
• Prompt Instruction Pruning - Prunes irrelevant contextual noise from prompts to maintain performance and adhere to token limits.
• Protocol Shells - Uses reusable templates with defined field operations to standardize how information is processed.
• RAG Context Pruning - Manages and prunes retrieved documents to reduce hallucinations and optimize token consumption in RAG pipelines.
• Structured Output Enforcements - Standardizes model output through the enforcement of symbolic formats and schema-defined protocol shells.
• Symbolic Prompt Structuring - Implements structured formats like JSON and Markdown to leverage internal symbolic mechanisms for improved reasoning.
• Symbolic Reasoning Formats - Leverages Markdown and JSON formats to improve abstract reasoning in large language models.
• Context Engineering - Handbook for context design, orchestration, and optimization.
• Prompt Engineering Resources - First-principles handbook for moving beyond prompts to context design.

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