When using an AI agent to look up information, we usually just dump an entire webpage, website, or directory into the model and let it "brute force" its way through to pick out a few potentially useful paragraphs.
This paper proposes a different approach:
1. Use a query retrieval system to push only the relevant paragraphs to the AI.
2. Consolidate and deduplicate the information from those sections.
This approach cuts costs and runs much faster.
This method solves the efficiency problem, but accuracy cannot be guaranteed. It relies entirely on the capability of the query system mentioned in the first point here.
Another way to improve data accuracy is to extract and transform the semantic layer before data entry. This does make writing data slower, but semantic data is different from transactional business data.
Semantic data doesn't necessarily need to be consumed by operational systems immediately. From a database perspective, it doesn't require OLTP characteristics. Therefore, a better approach might be to let this data undergo a period of processing and cleaning to become meaningful semantic information before making it available for subsequent retrieval.
I'd love to explore a question. If I have a standard elementary school math textbook, along with all the results of every correct and incorrect answer my child got on the textbook exercises and class tests, is it possible to train a model that can help me figure out what concepts my child is struggling with and have that model provide help?
Ever since Jev blew up, it's wild seeing so many models pop up right away. It looks like there is another new trend in models. It doesn't look like I'd have much use for that in my project(Cortrix), since what I need to solve is semantic accuracy.
Got it. I always have the model write the whole thing first and then I just do a little light editing. It has zero soul.
I'm looking forward to trying out your editor. When will it be officially available? But I have another question: English isn't my first language. What kind of impact can this editor have for non-English ecosystems? and if we add translation and LLM models on top of that, how they would interact is something I care about.
This paper proposes a different approach:
1. Use a query retrieval system to push only the relevant paragraphs to the AI. 2. Consolidate and deduplicate the information from those sections.
This approach cuts costs and runs much faster.
This method solves the efficiency problem, but accuracy cannot be guaranteed. It relies entirely on the capability of the query system mentioned in the first point here.
Another way to improve data accuracy is to extract and transform the semantic layer before data entry. This does make writing data slower, but semantic data is different from transactional business data.
Semantic data doesn't necessarily need to be consumed by operational systems immediately. From a database perspective, it doesn't require OLTP characteristics. Therefore, a better approach might be to let this data undergo a period of processing and cleaning to become meaningful semantic information before making it available for subsequent retrieval.