Demonstrate-Search-Predict: LLM Pipeline to Retriever Model

Watch a technical presentation from Stanford University exploring the Demonstrate-Search-Predict (DSP) methodology for integrating Large Language Models with retrieval systems. Learn how DSP enables the creation of sophisticated pipeline-aware demonstrations, intelligent passage searching, and grounded predictions by breaking down complex problems into manageable transformations. Explore the evolution from langchain to langgraph approaches, including the specification of state, agent nodes, and edge logic. Discover how frozen language models combine with retrieval models through in-context learning, eliminating the need for fine-tuning. Understand the system's self-configurable and self-optimizing capabilities, its use of graph theory for pipeline connections, and implementation of tools like GraphSAGE, GraphBERT, and PyTorch Geometric. Gain insights into how this advanced architecture transforms traditional template-based systems into dynamic, self-improving frameworks capable of multi-hop searches and complex reasoning.