skill-md-contextual-enrichment-in-llms-rag-evol

name: skill.md---contextual-enrichment-in-llms-(rag-evol description: Skill for AI agent capabilities

SKILL.md - Contextual Enrichment in LLMs (RAG Evolution)

Paper Reference

• arXiv: 2604.03174
• Title: Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models
• Utility Score: 0.85
• Authors: Shivangi Agarwal et al.
• Date: April 2026

Core Insights

Problem Addressed

LLMs limited by:

• Static knowledge
• Finite context windows
• Weakly structured causal reasoning

Augmentation Spectrum

Ranked by degree of structured context:

1. In-Context Learning & Prompt Engineering

   • Minimal structure
   • Ad-hoc prompting

2. Retrieval-Augmented Generation (RAG)

   • Document retrieval
   • Unstructured context injection

3. GraphRAG

   • Graph-structured retrieval
   • Entity relationship preservation

4. CausalRAG

   • Causal structure in retrieval
   • Reasoning-aware augmentation

Practical Applications

Deployment Decision Framework

Assess needs:
1. Knowledge freshness → RAG needed
2. Entity relationships → GraphRAG
3. Causal reasoning → CausalRAG
4. Context limits → Determine augmentation level

Choose augmentation based on:
- Task complexity
- Reasoning requirements
- Domain structure
- Latency constraints

Literature Screening Protocol

• Transparent filtering methodology
• Cross-paper evidence synthesis
• Higher-confidence vs. emerging results distinction

Key Takeaways

• Contextual enrichment is a spectrum, not a single technique
• More structure → better reasoning, higher cost
• RAG evolution: documents → graphs → causal
• Decision framework for deployment choices

Related Work

• Classic RAG implementations
• Graph-based knowledge systems
• Causal reasoning in NLP

Further Reading

• Full paper: https://arxiv.org/abs/2604.03174
• PDF: https://arxiv.org/pdf/2604.03174
• 4 tables comparing approaches

Description

SKILL.md - Contextual Enrichment in LLMs (RAG Evolution)

Activation Keywords

• rag-contextual-enrichment
• rag-contextual-enrichment 技能
• rag-contextual-enrichment skill

Tools Used

• read - Read documentation and references
• web_search - Search for related information
• web_fetch - Fetch paper or documentation

Instructions for Agents

Follow these steps when applying this skill:

Step 1: In-Context Learning & Prompt Engineering

Step 2: Retrieval-Augmented Generation (RAG)

Step 3: GraphRAG

Step 4: CausalRAG

Step 5: Understand the Request

Examples

Example 1: Basic Application

User: I need to apply SKILL.md - Contextual Enrichment in LLMs (RAG Evolution) to my analysis.

Agent: I'll help you apply rag-contextual-enrichment. First, let me understand your specific use case...

Context: Apply the methodology

Example 2: Advanced Scenario

User: Complex analysis scenario

Agent: Based on the methodology, I'll guide you through the advanced application...

Example 2: Advanced Application

User: What are the key considerations for rag-contextual-enrichment?

Agent: Let me search for the latest research and best practices...