kaleb cadenhead
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Two-Step Contextual Enrichment
TSCE is model-agnostic and increases LLM accuracy +20-30pp
This repo is for the demonstration of TSCE principles. - AutomationOptimization/tsce_demo TSCE is an open source framework that increases the accuracy and reproducibility of LLM's and AI agents. Run more than 4000 test prompts, noted an uplift of +10 - +30pp
About Two-Step Contextual Enrichment on Product Hunt
“TSCE is model-agnostic and increases LLM accuracy +20-30pp”
Two-Step Contextual Enrichment was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #48 on the daily leaderboard. This repo is for the demonstration of TSCE principles. - AutomationOptimization/tsce_demo TSCE is an open source framework that increases the accuracy and reproducibility of LLM's and AI agents. Run more than 4000 test prompts, noted an uplift of +10 - +30pp
Who hunted Two-Step Contextual Enrichment?
Two-Step Contextual Enrichment was hunted by kaleb cadenhead. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Two-Step Contextual Enrichment including community comment highlights and product details, visit the product overview.