Visualize Your RAG Data: EDA for Retrieval-Augmented Generation Curiosity: How can we visualize RAG embedding data to understand retrieval quality? What insights can interactive visualizations rev...

A Survey of Prompt Engineering Methods in Large Language Models Curiosity: What are the most effective prompt engineering techniques? How can we systematically understand and apply prompt engineer...

Microservices: Popular Architectural Style for Modern Applications Curiosity: How can we build scalable, maintainable applications? What makes microservices architecture effective for modern syste...

Today, I want to break down the 5 key layers that form the backbone of most modern software applications: As software systems become increasingly complex, understanding their architecture is cru...

The LLM Engineer’s Handbook: Building Production-Ready LLM Applications Curiosity: How do we bridge the gap between LLM research and production deployment? What engineering practices enable us to ...

RouteLLM ⇒ 𝗰𝘂𝘁 𝟱𝟬% 𝗼𝗳 𝗰𝗼𝘀𝘁 ✂️ Curiosity: What insights can we retrieve from this? How does this connect to innovation in the field? Curiosity: The LMSys team maintains the ChatbotArena, which is ...

GitHub: The Free University for Data Science Curiosity: How can we leverage GitHub as a learning platform? What knowledge can we retrieve from the vast repository of open-source data science resou...

Paper 👉 https://arxiv.org/pdf/2110.13048 If you don’t have too much data and the imbalance is not too extreme, the typical way to deal with it is to simply reweigh the samples such that the ...

ColPali: Efficient Document Retrieval with Vision Language Models Curiosity: How can we simplify PDF document retrieval in RAG systems? What happens when we embed document images directly instea...

3 comon methods Prompt Engineering Retrieval Augmented Generation (RAG) Parameter Efficient Fine-Tuning (PEFT) There are many more methods but these are the easiest and can result in majo...