Building ElectionGPT, a RAG powered chatbot grounded on candidate data

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Disclaimer: I am the co-founder of Neum AI, a data processing platform for vector embedding generation and data retrieval for RAG. With the upcoming elections 2024 coming up, we wanted to build a chatbot to allow people to interact with presidential candidates. We wanted the chatbot to be as authentic and unbiased as possible regarding each candidates' perspectives. To do this, we based each candidates KB on their own generated information including tweets, website and government plan. We then sprinkled in generally considered unbiased sources like Wikipedia and Ballotpedia that provide additional context. (Some re-ranking being done in the backend to favor candidate generated data sources) We leveraged our core platform (Neum AI) to consolidate the data into individual indexes for each candidate. These indexes are refreshed daily to pull in any new information. (Ex. latest tweets) You can access the open-source project for the chatbot here: https://github.com/NeumTry/electiongpt-frontend

Visit product View on Hacker NewsOct 31, 2023

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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