How to Build a State-of-the-Art Search Stack for LLMs: RAG, Reranking, and Reinforcement Learning

High-performance search infrastructure is crucial for AI systems, particularly those utilizing large language models (LLMs), as they depend heavily on external context to generate accurate and grounded responses. Retrieval-augmented generation (RAG) remains relevant, with a shift toward more sophisticated context engineering and strategic retrieval processes to enhance AI output quality. Modern AI agents benefit from multi-stage search architectures, where the initial broad retrieval is followed by a more precise and compute-intensive reranking stage to reorder and score documents based on relevance. Reranking is often ignored but crucial in reducing model errors and improving latency and token efficiency. New techniques, such as training rerankers with reinforcement learning, are emerging, allowing retrieval systems to adapt better to various use cases. Despite the evolving AI search ecosystem, challenges such as tool fragmentation and limited multimodal support persist, but next-generation platforms are addressing these by enabling frictionless pipelines. Investing in advanced retrieval and reranking systems is essential for improving product quality, performance, and user trust.