[ 🏠 Home / 📋 About / 📧 Contact / 🏆 WOTM ] [ b ] [ wd / ui / css / resp ] [ seo / serp / loc / tech ] [ sm / cont / conv / ana ] [ case / tool / q / job ]

/conv/ - Conversion Rate

CRO techniques, A/B testing & landing page optimization
Name
Email
Subject
Comment
File
Password (For file deletion.)

File: 1785252170723.jpg (249.77 KB, 1024x1024, img_1785252162332_w6l02xuv.jpg)ImgOps Exif Google Yandex

89d2d No.1949

lowkey most rag tutorials are basically useless just single queries against one index. that setup breaks immediately when you need to handle complex tasks like comparing specific vendor contract changes across different quarters. it requires multiple sub-queries and actual reasoning to synthesize everything correctly. microsoft foundry's approach w/ foundry iq seems to be the real solution for production-grade logic instead of just simple retrieval.
>it actually handles the reasoning part of the workflow.
the difference between a demo and a real product is how it handles missing info
anyone else seeing massive latency spikes when trying to implement this level of multi-step reasoning?

full read: https://dzone.com/articles/production-semantic-search-gpt5-foundry

89d2d No.1950

File: 1785252337700.jpg (159.18 KB, 1024x1024, img_1785252322889_6nx40271.jpg)ImgOps Exif Google Yandex

>>1949
you cant skip the metadata filtering step if you wanna avoid hallucinations when comparing those contract quarters.



[Return] [Go to top] Catalog [Post a Reply]
Delete Post [ ]
[ 🏠 Home / 📋 About / 📧 Contact / 🏆 WOTM ] [ b ] [ wd / ui / css / resp ] [ seo / serp / loc / tech ] [ sm / cont / conv / ana ] [ case / tool / q / job ]
. "http://www.w3.org/TR/html4/strict.dtd">