RAG (Retrieval-Augmented Generation)
Enhance AI models with real-time access to external knowledge and proprietary data
What is RAG?
Retrieval-Augmented Generation (RAG) is a technique that enhances large language models by connecting them to external knowledge sources. Instead of relying solely on information learned during training, RAG-enabled models can retrieve relevant information from databases, documents, or APIs in real-time to provide more accurate, current, and contextually relevant responses.
Think of RAG as giving an AI assistant access to a library, search engine, or company database. When you ask a question, the system first searches for relevant information from these external sources, then uses that retrieved context along with the model's inherent knowledge to generate a comprehensive, accurate response.
This approach solves key limitations of standalone language models: outdated information, lack of access to proprietary data, and hallucination of facts. RAG enables AI systems to work with current information and company-specific knowledge while maintaining the conversational and reasoning capabilities of foundation models.
How RAG Works
1. Knowledge Preparation
Documents, databases, and knowledge sources are processed and converted into vector embeddings—numerical representations that capture semantic meaning. These embeddings are stored in a vector database for efficient searching.
2. Query Processing
When a user asks a question, the system converts the query into a vector embedding using the same process, enabling semantic similarity matching rather than just keyword searching.
3. Retrieval
The system searches the vector database to find the most relevant documents or data chunks based on semantic similarity to the user's query, retrieving the top matching results.
4. Augmented Generation
The retrieved information is provided as context to the language model along with the original query. The model then generates a response based on both its training knowledge and the retrieved external information.
Example RAG Workflow
Business Applications
Customer Support
RAG-powered chatbots access product documentation, help articles, and support tickets to provide accurate, up-to-date customer assistance without human intervention.
Internal Knowledge Management
Employees can query company policies, procedures, and institutional knowledge using natural language, making information accessible across the organization.
Research & Analysis
Analysts can query vast databases of market research, financial reports, and industry data to generate insights and reports with current, verified information.
Compliance & Legal
Legal teams can search through regulatory documents, case law, and company policies to ensure compliance and provide accurate legal guidance.
Implementation Technologies
Vector Databases
Specialized databases like Pinecone, Weaviate, or Chroma that efficiently store and search vector embeddings for semantic similarity matching.
Embedding Models
Models that convert text into vector representations, enabling semantic search. OpenAI's text-embedding-ada-002 and sentence-transformers are popular choices.
Orchestration Frameworks
Tools like LangChain, LlamaIndex, and Haystack that simplify building RAG applications by handling retrieval, prompting, and generation workflows.
RAG Implementation Best Practices
Data Preparation
- • Chunk documents appropriately (usually 200-1000 tokens)
- • Maintain metadata for filtering and context
- • Regularly update knowledge base with fresh data
- • Clean and preprocess data for quality
Optimization
- • Tune retrieval parameters for relevance
- • Implement hybrid search (semantic + keyword)
- • Monitor and evaluate response quality
- • Use reranking models for better results
Related AI Terms
Foundation Models
The base AI models that RAG enhances with external knowledge
Context Window
The amount of information that can be provided to the model
Prompt Engineering
Crafting effective prompts that work with retrieved information
Fine-Tuning
Alternative approach to customizing AI models for specific tasks