A GitHub Action for helping RAG apps with CI/CD

Hacker News

Use this GitHub action to easily integrate RAG system evaluation into your CI/CD process. The README contains full instructions and an example. This action runs every time you create a PR and for each subsequent commit to the branch. Simply point it to a file that contains test questions and optional reference answers and context. It evaluates the questions and then scores the RAG generated response against the provided references. After it computes the scores across 6 different metrics, it generates a table that is displayed as a comment on your PR.

Visit product View on Hacker NewsFeb 23, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, context · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Strong signals: apps, answers · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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