To retrieve relevant information, RAG models can access a wide range of external databases, such as public databases (including Wikipedia and PubMed), industry-specific databases (like LexisNexis and Medline), proprietary databases (containing internal documents and reports), and news and media sources. By incorporating data from these diverse sources, RAG models ensure that generated responses are accurate and current.
Benefits of RAG
RAG’s ability to ensure data accuracy saudi arabia whatsapp number data carries a range of benefits that make generative AI’s use in the enterprise more trustworthy and practical.
Decreases the chance of AI hallucinations. RAG lowers the opportunity for AI hallucinations by firmly establishing reactions based on verifiable facts. AI hallucinations occur in 3-10% of responses generated by AI models, often due to biases or incomplete training data. By incorporating real-time data, RAG ensures that responses are based on current and accurate information, significantly reducing the risk of hallucinations.
Enhances relevance and accuracy. RAG gives AI models access to a larger body of knowledge by combining public and private data sources. This guarantees that answers are correct and pertinent to the request’s context. Organizations can use RAG to incorporate their private data, certifying that AI-generated solutions align with their needs and expertise.
Ensures data privacy and compliance. sources without exposing them to public LLMs. This feature safeguards the security of sensitive data. It conforms with data privacy laws such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act of 1996 (HIPAA). Businesses can use their data to customize AI models without sacrificing security or privacy.