Modern RAG systems are often reduced to a simple pipeline: embed documents, perform vector search, retrieve the top-k chunks, and send them to an LLM. In practice, retrieval is rarely that simple. Exact identifiers, technical terminology, semantic paraphrases, lost document context, and multi-step questions can all expose weaknesses in a purely vector-based approach. This talk explores how modern retrieval systems move beyond basic semantic search through hybrid retrieval, combining sparse and dense methods, contextual retrieval, reranking, query decomposition, and adaptive or agentic retrieval. Rather than presenting a single “best” architecture, we will examine the trade-offs between relevance, recall, latency, cost, and complexity, and look at when each technique is actually useful. The central idea is simple: a RAG system does not need to find the most similar chunks; it needs to retrieve the right evidence for the question. We will build this idea step by step and show how retrieval quality, rather than simply choosing a more powerful LLM, can determine the quality of the final answer.
Leon Achata is a Generative AI Engineer focused on building production-grade LLM applications, RAG systems, and intelligent agents. He currently works on enterprise GenAI solutions at Apple via TCS, designing LLM-powered workflows, retrieval systems, and agent orchestration for large-scale international environments. Previously, he worked on multi-agent systems, RAG pipelines, and AI infrastructure across AWS and other cloud platforms. His background combines software engineering, AI/ML, and biomedical engineering, giving him a practical perspective on applying LLMs to complex real-world problems.
