Can prompting and context engineering be enough to build a production-ready search and recommender system? We compare keyword, vector and hybrid retrieval methods, then introduce Superlinked’s mixture-of-encoders architecture, which combines specialized encoders with LLM query understanding for better relevance. With AI-driven coding workflows, we show how prompting can scaffold components, guide evaluation and accelerate iteration. Because Superlinked provides the structure and resources to handle multi-attribute and metadata-aware queries out of the box, teams can move from prototype to production much faster. We also cover integrations with LangChain and LlamaIndex, making it easy to connect with existing pipelines. Attendees will learn practical strategies to build high-quality search and recommender systems quickly using prompting and the open source Superlinked library.

Filip Makraduli is a machine learning engineer with a strong background in AI systems, vector search, and large language models (LLMs). He holds a Master’s degree in Biomedical Data Science from Imperial College London. Currently, Filip works as a founding developer relations engineer at Superlinked, where he focuses on building real-time, multi-attribute search and recommendation systems. His work emphasizes the use of multi-encoder architectures to enhance retrieval quality and reduce reliance on reranking strategies. In the past, Filip worked as a data scientist at Marks & Spencer, where he contributed to AI-driven solutions for retail. He has also held machine learning engineering roles across several UK-based startups, focusing on applied AI and product-oriented ML development. In addition to his industry work, Filip has been active in the open-source community, particularly around LLM tooling and pipelines. He has delivered various talks on practical machine learning applications.