Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG)
What is RAG?
Retrieval Augmented Generation (RAG) is an innovative architecture that enhances the capabilities of Large Language Models (LLMs) like ChatGPT. It integrates an information retrieval system into the generative process, providing a grounding layer of data that the LLM can use when formulating responses. This approach allows for more accurate and contextually relevant outputs by leveraging external, verified information sources. RAG operates by first retrieving pertinent information from a vast repository of knowledge and then using a generative model to formulate responses based on this data. The synergy between the retrieval and generative components enables RAG to produce high-quality, informative, and relevant answers, which is particularly useful in applications such as AI chatbots, question-answering systems, and content creation. By incorporating RAG, developers can create AI applications that are not only more responsive to user queries but also provide answers that are grounded in factual information, thereby reducing the spread of misinformation and bias often associated with purely generative models.
- RAG 101: Retrieval Augmented Generation - YouTube —youtube.com
- An intro to RAGHack, a global hackathon to develop apps using LLMs and RAG. A large language model (LLM) like GPT-4 can be used for summarization, translatio…
Source: YouTube
Related:
External links:
- Retrieval Augmented Generation (RAG) — promptingguide.ai
- Pavan Belagatti on LinkedIn: #rag | 23 comments —linkedin.com
- Which #RAG is more superior: Graph RAG or Vector RAG?
- Pavan Belagatti on LinkedIn: #rag —linkedin.com
- Better context for your #RAG applications with Contextual Retrieval?
- Pavan Belagatti on LinkedIn: #rag #ai —linkedin.com
- Vector vs Graph: The #RAG Showdown Reshaping #AI Applications!
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Taxonomy
- RAG
- Architecture Components
- Retrieval System
- Vector Stores
- Dense Vector Databases (Pinecone, Weaviate, Milvus)
- Hybrid Search Databases (Chroma, Qdrant)
- Graph Vector Databases (Neo4j Vector Index)
- Retrieval Strategies
- Dense Retrieval
- Sparse Retrieval (BM25, TF-IDF)
- Hybrid Retrieval
- Multi-stage Retrieval
- Multi-query Retrieval
- Query Processing
- Query Rewriting
- Query Expansion
- Query Decomposition
- Hypothetical Document Embeddings
- Vector Stores
- Embedding Models
- General Purpose Embeddings
- OpenAI Embeddings (text-embedding-ada-002, text-embedding-3)
- Cohere Embeddings
- Sentence Transformers (MPNet, BERT)
- E5 Embeddings
- Domain-Specific Embeddings
- Legal Embeddings
- Medical Embeddings
- Scientific Embeddings
- Multi-Modal Embeddings
- Text-Image Embeddings (CLIP)
- Cross-Modal Retrieval Models
- General Purpose Embeddings
- Generation System
- LLM Types
- Open-Source Models (Llama, Mistral, Falcon)
- Proprietary Models (GPT, Claude)
- Quantized Models
- Locally Deployed Models
- Prompt Engineering
- Few-Shot Prompting
- Chain-of-Thought
- ReAct Prompting
- Context Window Management
- Output Optimization
- Output Parsing
- Output Formatting
- Hallucination Detection
- LLM Types
- Document Processing Pipeline
- Document Ingestion
- Web Scraping
- PDF Processing
- OCR Technologies
- Structured Data Extraction
- Document Chunking
- Fixed-Size Chunking
- Semantic Chunking
- Recursive Chunking
- Sliding Window Chunking
- Document Preprocessing
- Text Normalization
- Noise Removal
- Metadata Extraction
- Language Detection
- Document Ingestion
- Retrieval System
- RAG Architectures
- Basic RAG
- Single-Step Retrieval
- Direct Context Insertion
- Simple Ranking
- Advanced RAG
- Multi-Step RAG
- Iterative RAG
- Recursive RAG
- Agent-Based RAG
- Hybrid Architectures
- RAG with Fine-Tuning
- RAG with Knowledge Graphs
- RAG with Structured Data
- Multi-Modal RAG
- Basic RAG
- Evaluation and Metrics
- Retrieval Metrics
- Precision
- Recall
- F1 Score
- Mean Average Precision (MAP)
- Normalized Discounted Cumulative Gain (NDCG)
- Generation Metrics
- BLEU, ROUGE, METEOR
- Perplexity
- BERTScore
- Factual Consistency
- Hallucination Rate
- End-to-End Evaluation
- Human Evaluation
- Automated Evaluation Pipelines
- A/B Testing
- Domain-Specific Benchmarks
- Retrieval Metrics
- Optimization Techniques
- Indexing Optimization
- Quantization
- Dimensionality Reduction
- Approximate Nearest Neighbor Algorithms
- Clustering Techniques
- Computational Efficiency
- Batching Strategies
- Caching
- Model Distillation
- Inference Optimization
- Quality Improvement
- Reranking
- Filter-and-Refine
- Contextual Compression
- Ensemble Methods
- Indexing Optimization
- Applications and Use Cases
- Enterprise Applications
- Customer Support
- Knowledge Management
- Compliance and Policy
- Internal Search
- Developer Tools
- Code Assistance
- Documentation Generation
- Bug Analysis
- Testing
- Content Creation
- Copywriting
- Content Enhancement
- Research Assistance
- Creative Writing
- Domain-Specific Applications
- Legal (Contract Analysis, Case Law)
- Healthcare (Medical Research, Patient Records)
- Finance (Market Analysis, Regulatory Compliance)
- Education (Tutoring, Curriculum Development)
- Enterprise Applications
- Challenges and Limitations
- Technical Challenges
- Context Window Limitations
- Embedding Quality Issues
- Retrieval Efficiency at Scale
- Cross-Language Retrieval
- Quality Issues
- Hallucinations
- Outdated Information
- Source Attribution Problems
- Knowledge Conflicts
- Ethical Considerations
- Privacy Concerns
- Copyright and Intellectual Property
- Bias in Retrieved Content
- Data Governance
- Technical Challenges
- Deployment and Infrastructure
- Cloud Deployment
- Serverless Architectures
- Containerization
- Managed Services
- Auto-Scaling
- On-Premises Deployment
- Hardware Requirements
- Air-Gapped Solutions
- Enterprise Integration
- Private Cloud Options
- Monitoring and Maintenance
- Performance Tracking
- Quality Assurance
- Model Updating
- Data Refreshing
- Cloud Deployment
- Integration Points
- API Integration
- REST APIs
- GraphQL
- gRPC
- Webhooks
- Application Integration
- Chat Applications
- Search Systems
- Knowledge Bases
- Content Management Systems
- Workflow Integration
- Business Process Automation
- Decision Support Systems
- Intelligent Assistants
- Research Workflows
- API Integration
- Future Trends
- Model Improvements
- Smaller, Faster Models
- Multi-Modal RAG
- Self-Improving Systems
- Domain Adaptation
- Architectural Innovations
- Knowledge Graphs + RAG
- Multi-Agent RAG Systems
- Continuous Learning
- Personalized RAG
- Emerging Applications
- Scientific Discovery
- Complex Decision Support
- Multimodal Understanding
- Collaborative Work Enhancement
- Model Improvements
- Architecture Components