Building a RAG system is complex and it’s important that we measure the accuracy and performance of each component and use the right design and model during the development phase itself.

To build a RAG using libraries that would give flexibility over a rigid structure that may (or may not) be needed for complex architectures. It clearly isn’t needed for small size apps.

A deep-dive into RAG components built using FASTAPI, Adalflow, Groq, HuggingFace Transformers, PDFPlumber.

AIP is Artificial Intelligence Platform provided by Palantir to build business use cases using AI. Clubbed with other platforms that Palantir offers like Foundry, Pipeline Builder etc. even a non-technical person can build a sophisticated solution in no-time.

Imagine a system that could take a single piece of information and seamlessly transform it into tailored content for Instagram, LinkedIn, Twitter, and beyond. This isn’t a marketer’s pipe dream, but a tangible reality made possible through innovative social media pipelines built on Palantir AIP.

Recommendation systems date back to the early 90's where they were first developed by researchers to suggest important documents to the readers based on the review ratings provided by other users.It became mainstream after tech giants like Google, Amazon, Netflix, Spotify and others integrated it with their systems to recommend relevant content and products to the users. Especially as personalized recommendations gained traction so did recommendation system algorithms. These systems are behind suggesting movies, ads, youtube videos, music to the users based on their interests or we can say based on their predicted interests.