Best way to use a model to extract parameters from a question?
TLDR; what is the best way to provide additional context to an LLM about a list of games and categories, so that when a user asks a question, it can identify what the user is asking about and extract these properties, almost as parameters?Hi all! Thank you for humoring my beginner question.I am familiar with LLMs, though have never fine-tuned one (and it is seeming I may have to go that route).I have a bot I’ve made for a speedrunning community, right now it uses commands (such as !foo). I’d like to move the bot to utilizing an LLM, specifically a chat model, so users can naturally ask questions to the bot. It would need to be able to identify and extract key pieces of information from the user’s question, including: the subject (i.e., “best_time”), the game, and the category (i.e., 100%).The LLM should be able to return an output similar to the following:{"game": "GAME", "category": "CATEGORY", "subject": "best_time"}, etc…I am unsure if this makes sense.I have been able to get LLMs to be able to summarize the user’s query into a subject. Game and category it cannot figure out.What could I do to improve the results of an LLM in understanding what a game and category is? I have access to all the games and categories that are speedran, but I would need to construct training data out of it.Do I need to fine-tune? Use embeddings? Token classification? Essentially…what would be the best route to give my LLM context about what a game and category is, so it can more reliably return the information I need? I have exhausted my prompt tuning options.Thank you!