Why RAG Is Needed
Understanding why RAG exists explains when to use it and what problem it solves.
8 min•By Priygop Team•Updated 2026
The Problem RAG Solves
- Knowledge cutoff: LLMs only know about events that happened before their training data cutoff date
- Hallucinations: when asked about something not in training data, LLMs may invent plausible-sounding but wrong answers
- Private data: LLMs do not know about your company's internal documents, policies, or specific product details
- Up-to-date information: LLMs cannot access current prices, live stock data, recent news, or changing information
- Large documents: you cannot always fit an entire document library into a model's context window
Diagram
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Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
What RAG Enables
- AI customer support that accurately answers questions about your specific products and policies
- An internal knowledge base assistant that knows your company's processes and documents
- A research assistant that can answer questions about a specific set of scientific papers
- A legal assistant that answers questions based on specific contracts or case files
- A product assistant that accurately describes features and specifications from your documentation
Key Takeaways
- Understanding why RAG exists explains when to use it and what problem it solves.
- Knowledge cutoff: LLMs only know about events that happened before their training data cutoff date
- Hallucinations: when asked about something not in training data, LLMs may invent plausible-sounding but wrong answers
- Private data: LLMs do not know about your company's internal documents, policies, or specific product details