Page Comparison

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A go-to-market slide for a preseed-seedstage startup in fintech.

And you could imagine different rules having different texts but in a similar format:

A problem slide for a series b startup in health care.
A solution slide for a angel-phase pre-seed startup in e-commerce tech.

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• Chunker - Our document chunker smart-chain will need to take in the original documents, and transform them into small, bite sized chunks of text, each containing a single best practice or idea that was extracted from the document. This isn’t really breaking apart the original document, but rather wholly digesting it and transforming it into something new - one paragraph descriptions of best practices

• Matching Text - Our matching text smart chain needs to take each of the best-practice paragraphs generated by the chunker, and transform them into our standard matching text format described above, e.g. A problem slide for a series b startup in health care. We may design the prompt to generate a bunch of potential matching texts for each best-practice paragraph that it processes

• Qualifying Text - We can stick with the default qualifying text, which would just be a summary of the original document that the best-practice was derived from, in case that information is relevant

• Query Transformer - Our use scenario for our knowledge base involves uploading the raw text of each presentation slide. We would need to make a custom query transformer which can take that raw-text and convert it into our standard matching format described above, e.g. A problem slide for a series b startup in health care.

• Reranker - Our matching text is relatively simple and close ended, so we may want to disable the reranker by switching to the identity reranker and rely on the matching text alone. This reduces the cost of knowledge base queries and improves the response speed.

• Filterer - Given the very closed-ended nature of our matching text, we may find that by default, all the match scores end up always being very high, e.g. > 0.9 even between matching texts that are supposed to be different and not match. Therefore, we may have to adjust the default filtering smart chain to calibrate it for our specific use case, setting a cutoff that may be as high as 0.95 or 0.97

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