Both these documents were split by page, each of which was embedded using the text-embedding-3-large model, the stateof-the-art embedding model by OpenAI at the time of writing . The resulting embeddings were indexed using FAISS , a library designed to readily cluster dense vectors. These clustered vectors permit a FAISS similarity search between the user prompt and the indexed embedding vectors to quickly retrieve relevant documentation. The K most similar pages (one of our hyperparameters) are retrieved and added to the user prompt for the model to make use of during the ORCA input generation.