RAG (Retrieval-Augmented Generation) Design Patterns

Basic RAG Types

Corrective RAG

Corrective RAG acts as a real-time fact-checking system that validates generated responses against trusted sources. It employs an error-detection module to ensure accuracy and reliability, making it particularly valuable in fields where precision is crucial.

Real-time fact-checker
Validates responses against reliable sources
Error-detection module
Best for: Healthcare, law, finance

Speculative RAG

This system anticipates user needs by predicting potential queries and pre-fetching relevant data. By proactively preparing responses, it significantly reduces latency and improves user experience in dynamic environments.

Anticipates user needs
Pre-fetches data based on predicted queries
Reduces response times
Ideal for: E-commerce, customer service, news delivery

Agentic RAG

Agentic RAG creates a personalized experience by learning and evolving based on user interactions. It continuously refines its database and response patterns to better match individual user preferences and behaviors.

Evolves with user preferences
Dynamically refines database
Creates personalized experiences
Perfect for: Retail, entertainment, content curation

Self-RAG

Self-RAG implements an autonomous architecture that continuously evaluates and improves its own performance. It uses self-reflection mechanisms to optimize retrieval strategies and response quality over time.

Self-evaluating architecture
Continuous improvement focus
Iterative refinement
Suitable for: Finance, forecasting, logistics

Adaptive RAG

This type excels in dynamic environments by making real-time adjustments to its responses based on changing contexts. It maintains relevance and accuracy even as situations evolve during interactions.

Real-time context adjustments
Dynamic scenario handling
Flexible response system
Best for: Ticketing, supply chain, event management

Advanced Implementation Types

Refeed Feedback RAG

This system creates a continuous improvement loop by incorporating direct user feedback into its learning process. It uses interaction data to enhance future responses and adapt to user needs.

Learns from direct user feedback
Interactive improvement system
Continuous refinement
Ideal for: Customer service applications

Realm RAG

Realm RAG combines sophisticated retrieval mechanisms with deep language model understanding. It excels in technical domains where precise comprehension and accurate information retrieval are critical.

Combines retrieval with LLM understanding
Deep contextual comprehension
Technical domain expertise
Perfect for: Legal, technical documentation

Raptor RAG

Using a hierarchical, tree-based structure for data organization, Raptor RAG enables swift and precise information access. It’s particularly effective in scenarios requiring quick navigation of complex data hierarchies.

Hierarchical data organization
Tree-based structure
Quick precise access
Best for: Healthcare diagnostics, e-commerce categorization

Replug RAG

Replug RAG specializes in integrating and managing external data sources in real-time. It maintains up-to-date information by continuously syncing with live data feeds and external systems.

External data source integration
Real-time updates
Live data handling
Suitable for: Financial platforms, weather forecasting

Memo RAG

This system maintains contextual awareness across multiple interactions by storing and utilizing conversation history. It creates more coherent and contextually appropriate responses over extended interactions.

Context retention across sessions
Conversation memory
Coherent response tracking
Ideal for: Education platforms, customer support

Specialized Processing Types

RETRO RAG

RETRO RAG leverages historical context and past interactions to inform current responses. It provides comprehensive perspectives by integrating historical knowledge with current queries.

Historical context leverage
Comprehensive perspective
Past interaction integration
Best for: Knowledge management, legal research

Auto RAG

This automated system minimizes human intervention while maintaining high accuracy. It independently handles data retrieval and response generation with minimal oversight requirements.

Automated retrieval system
Minimal human oversight
Dynamic data handling
Perfect for: News aggregation, content platforms

Iterative RAG

Through multiple refinement steps, Iterative RAG progressively improves response quality. It implements feedback loops to enhance accuracy and relevance with each iteration.

Multi-step refinement
Progressive improvement
Feedback-based learning
Ideal for: Technical support, troubleshooting

Generative AI RAG

This creative-focused system combines retrieval capabilities with generative AI to produce original content. It analyzes trends and patterns to inform creative output.

Creative content generation
Original response creation
Trend analysis integration
Best for: Marketing, content creation, branding

Context Cache RAG

Specialized in maintaining consistent context throughout user sessions, this system ensures coherent interactions over time. It efficiently manages and utilizes cached contextual information.

Memory maintenance
Contextual consistency
Session continuity
Suitable for: Educational tools, long-term interactions

Advanced Analysis Types

Grokking RAG

Focused on deep understanding and complex data synthesis, Grokking RAG excels at providing intuitive explanations for complex topics. It’s particularly valuable in research and technical documentation.

Deep understanding focus
Complex data synthesis
Intuitive explanations
Perfect for: Scientific research, technical documentation

Replug Retrieval Feedback RAG

This system optimizes external source connections while maintaining accuracy through continuous feedback loops. It’s particularly effective in scenarios requiring real-time data accuracy.

External source optimization
Continuous connection refinement
Real-time accuracy
Best for: Financial data, logistics

Attention Unet RAG

Specializing in detailed analysis and data segmentation, this system provides precise focus on specific aspects of complex data. It’s particularly useful in specialized technical applications.

Granular data segmentation
Detailed analysis
Precision focus
Ideal for: Medical imaging, geospatial analysis

Performance and Compliance Types

Cost-Constrained RAG

Designed for efficiency, this system optimizes resource usage while maintaining performance. It’s ideal for organizations with specific budget limitations or resource constraints.

Budget-optimized retrieval
Resource efficiency
Performance balancing
Best for: Small businesses, educational institutions

Rule-Based RAG

Implementing strict compliance and regulatory adherence, this system ensures all responses follow predefined guidelines and rules. It’s crucial for regulated industries.

Compliance enforcement
Regulatory adherence
Guideline following
Ideal for: Financial advisory, healthcare guidance

XAI RAG

Focusing on transparency and explainability, this system provides clear reasoning paths for all decisions and responses. It’s essential in scenarios requiring decision justification.

Explainable decisions
Transparency focus
Clear reasoning paths
Best for: Healthcare decisions, legal advice

Selection Guidelines

Consider these factors when choosing a RAG type:

Specific use case requirements
Budget and resource constraints
Performance needs
Regulatory compliance requirements
Explainability needs
Integration capabilities
Scalability requirements

References

MarkTechPost. (2024). “Retrieval-Augmented Generation (RAG): Deep Dive into 25 Different Types of RAG”
- Article Link: https://www.marktechpost.com/2024/11/25/retrieval-augmented-generation-rag-deep-dive-into-25-different-types-of-rag/
Medium Article. “Mastering the 25 Types of RAG Architectures: When and How to Use Each One”
- https://medium.com/@rupeshit/mastering-the-25-types-of-rag-architectures-when-and-how-to-use-each-one-2ca0e4b944d7
LinkedIn Post by Bhavishya Pandit. “25 Types of RAG”
- https://www.linkedin.com/posts/bhavishya-pandit_25-types-of-rag-ugcPost-7261595005167796224-FjXW/
Research Papers:
- “Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection” arXiv:2401.15884v2
- “RETRO: Retrieval-Enhanced Transformer” arXiv:2310.11511
- “Attention Unet: A Fully Convolutional Neural Network for Medical Image Segmentation” arXiv:2409.05591
- “Realm: Retrieval-Augmented Language Model Pre-Training” arXiv:2002.08909
- “RAG vs Fine-tuning: Pipeline, Evaluation and Learnings” arXiv:2410.20878
- “Improving Language Understanding by Generative Pre-Training” arXiv:2301.12652

Paradigms of RAG Agentic RAG