LLM-Powered Recommendation System

To deliver high accuracy for business offers generation, we integrated Qdrant vector DB with an LLM (Claude Sonnet 3) to perform retrieval-augmented generation (RAG), ensuring the system retrieves and contextualizes relevant data points before generating personalized product recommendations.

Engineered prompts tailored to yield highly accurate, contextually relevant recommendations and enhance the reasoning capabilities of the LLM, utilizing state of the art prompting techniques such as Chain-of-Thought (CoT) and Meta prompting.

Our solution harnesses the power of Qdrant vector DB and advanced prompt engineering to deliver swift, precise filtering—driving exceptional speed and efficiency in opportunity generation.

Developed a text2query system that transforms natural language product and customer descriptions into a custom ElasticSearch-like query format, enabling users to intuitively filter products and customer profiles for enhanced interaction flexibility.

Incorporated information on global events via Web Search services, allowing the recommendation engine to adjust suggestions based on relevant and impactful current events.

Leveraged the LLM to dynamically create and populate nodes within a knowledge graph, enabling a structured and interconnected representation of customer preferences, product information, and interactions, further enhancing recommendation quality.