Current Challenges in Recommendation Systems:

• Unable to understand nuanced requests with both preferences and exclusions
• Often suggest irrelevant content due to poor understanding of user intent
• Prone to hallucinations where AI invents non-existent options
• Require structured inputs rather than natural language

Our RAG Solution Transforms This Experience:

• Understands natural requests like "A movie about wizards but not Harry Potter"
• Grounds all recommendations in actual data stored in the vector database
• Delivers semantically relevant results that honor both likes and dislikes
• Scales effortlessly as your content library grows