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Projects like Prob.lm come to life thanks to the collective effort of a passionate and collaborative team.
Here’s a quick look at the people who’ve contributed their time, skills, and energy to make this project what it is!
Emerging developers who brought fresh perspectives, energy, and a hunger to learn.
Rooted in experience, guiding us with valuable feedback, our mentors helped us build more than just a project. They helped us build confidence, perspective, and a way forward we’re proud to carry with us.
The idea for Prob.lm emerged from a well-documented challenge in information retrieval: as digital content grows exponentially, users increasingly struggle to extract precise, relevant data from large documents and datasets.
Studies show that knowledge workers spend up to 30% of their time searching for information, significantly impacting productivity and decision-making. (Source: IDC - "The High Cost of Not Finding Information")
Traditional keyword search tools often return irrelevant or incomplete results due to limited semantic understanding, leading to user frustration and inefficiency.
Our solution leverages Retrieval Augmented Generation (RAG) — A hybrid search RAG assistant that combines large language models with targeted document retrieval — to deliver factually grounded, contextually relevant answers from files attached as context by users.