Forward-Looking Active Retrieval Augmented Generation (FLARE) is an advanced methodology designed to enhance the precision and reliability of Large Language Models (LLMs) by actively incorporating verified external information during content generation. This approach addresses the common issue of "hallucination," where LLMs generate incorrect or baseless content, especially in complex tasks like long-form question answering and open-domain summarization. Unlike traditional models that rely on a single retrieval of information, FLARE employs multiple retrievals throughout the generation process, adapting to new contexts and ensuring the integration of relevant data. It operates in two modes: FLARE Instruct, which prompts the model to pause and retrieve necessary information, and FLARE Direct, which refines low-confidence information by generating implicit or explicit queries. The implementation details include the use of tools like LanceDB and vector databases, showcasing the potential for real-world applications and inspiring further exploration into this innovative technology.