Pongo – 80% Fewer LLM Hallucinations with one LoC

Hacker News

We’re building Pongo, a new retrieval layer that can cut LLM hallucinations by as much as 80% in RAG pipelines. You can see the full benchmarks on our website, where we saw RAG errors/hallucinations fall from ~19% to ~3% after adding Pongo. This has huge implications for AI applications, getting the right answer can be the difference between a user churning and adopting a product. For a 3 step agent, each step having a success rate of 80% compared to 97%, is the difference between a 51% and 92% success rate for the workflow as a whole. Pongo sits at the end of existing retrieval pipeline, whether it’s a vector database or a lexical search engine. You send in your top 100-300 results along with the query, then it uses a mix of models and retrieval methods to score and order the results in as little as 0.5s. The API is just 1 line of code. The accuracy difference comes from a two main factors 1. Surfacing relevant results that the initial search ranked outside of the top 10 results. 2. Moving the right results from ranks 5-15 to ranks 1-3, which significantly decreases strain on the LLM’s attention window. This jump in performance stems from out multi-model approach since each method has non-overlapping failure cases. We utilize multi-vector models, cross-encoder models, and sparse vector results in our ranking algorithm. This is a pretty compute intensive process and took quite a bit of work to get this to run with as little latency as it does. Would love to get some feedback, and see how it holds up in your projects.

Visit product View on Hacker NewsApr 25, 2024

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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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

13%13% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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