Hey, creator of above implementation here.

You're right that there's lots of ways accuracy could feasibly be improved, by using more varied APIs, navigating to search results and creating embeddings of the resulting website etc. Ultimately, a lot of this kind of more advanced chaining of LLM and API requests can be done with libraries like langchain.

For this one, i wanted to show how effective a much more simple approach can be. For search results, i simply chain together the returned google "snippets" and inject the resulting string back into the prompt. Often times, this means there can actually be conflicting information, such as for example dates talking about events adjacent to but ultimately irrelevant to the search query. However, this is where GPT is generally doing an excellent job of picking out the correct bit of info, so no more sophisticated filtering or parsing by the app is required. Just giving a raw dump of the search results to the model.