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Patronus AI

1 post about Patronus AI. Every summary links to the original.

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MongoDB
Building AI with MongoDB and Patronus AI
Large language models can be unreliable, with serious consequences in regulated industries. Patronus AI, an industry-first automated evaluation platform, enables engineers to score and benchmark LLM performance on real-world scenarios, generate adversarial test cases, and monitor hallucinations and PII. Research using FinanceBench found that state-of-the-art LLMs hallucinated or incorrectly answered up to 81% of financial analysts’ questions, even with RAG and external context. Patronus partnered with MongoDB Atlas to offer a RAG evaluation API and published a 10-minute guide demonstrating evaluation of a RAG system built with LlamaIndex and Atlas Vector Search on an SEC 10-K filing. Developers can improve RAG performance by adjusting indexes, chunking sizes, prompts, or fine-tuning embedding models, and should continuously test to maintain improvements.

The best engineering reads, once a week.

Topic

1 post about Patronus AI. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

MongoDB
Building AI with MongoDB and Patronus AI
Large language models can be unreliable, with serious consequences in regulated industries. Patronus AI, an industry-first automated evaluation platform, enables engineers to score and benchmark LLM performance on real-world scenarios, generate adversarial test cases, and monitor hallucinations and PII. Research using FinanceBench found that state-of-the-art LLMs hallucinated or incorrectly answered up to 81% of financial analysts’ questions, even with RAG and external context. Patronus partnered with MongoDB Atlas to offer a RAG evaluation API and published a 10-minute guide demonstrating evaluation of a RAG system built with LlamaIndex and Atlas Vector Search on an SEC 10-K filing. Developers can improve RAG performance by adjusting indexes, chunking sizes, prompts, or fine-tuning embedding models, and should continuously test to maintain improvements.

The best engineering reads, once a week.