Why Retrieval Augmented Generation (RAG)?
The Challenge with LLMs
Large Language Models (LLMs) face several key limitations:
- They can only access information from their training data
- Their knowledge becomes outdated after training
- They can produce hallucinations or incorrect information
- They lack reliable access to proprietary or domain-specific knowledge
Enter RAG: A Solution
Retrieval Augmented Generation (RAG) addresses these limitations by:
- Retrieving relevant information from external sources in real-time
- Augmenting LLM prompts with this retrieved context
- Generating responses based on both the model’s knowledge and retrieved data
Key Benefits
1. Up-to-date Information
- Access to current data beyond training cutoff
- Real-time information retrieval
- Dynamic knowledge integration
2. Reduced Hallucinations
- Grounded responses in factual data
- Verifiable information sources
- Enhanced accuracy and reliability
3. Domain Adaptation
- Integration with specialized knowledge bases
- Support for proprietary information
- Customization for specific use cases
4. Cost Efficiency
- No need for constant model retraining
- Lower computational requirements
- Easier maintenance and updates
Use Cases
- Question Answering Systems
- Customer Support
- Document Analysis
- Research Assistance
- Content Generation
- Knowledge Management