Daily Dose of Data Science | Avi Chawla | Substack·1y9 RAG, LLM, and AI Agent Cheat Sheets
This post provides visual cheat sheets for AI engineers covering various topics, including Transformer vs. Mixture of Experts in LLMs, fine-tuning techniques, RAG vs Agentic RAG, strategies for chunking in RAG, levels of agentic AI systems, and more. These resources are designed to help cultivate essential skills for developing impactful AI and ML systems in the industry.
SwirlAI·1yThe evolution of Modern RAG Architectures.
The post delves into the evolution of Retrieval Augmented Generation (RAG) architectures, discussing their development from Naive RAG to advanced techniques, including Cache Augmented Generation (CAG) and Agentic RAG. It highlights the challenges addressed at each stage, advanced methods to improve accuracy, and the potential future advancements in RAG systems.
BigData Boutique blog·1yBuilding RAG Apps Without the Bloat: Meet Shraga
Discover Shraga, an open-source framework designed by BigData Boutique to simplify and scale GenAI applications without the overhead of popular RAG frameworks. Shraga offers minimal boilerplate, supports multiple LLM providers, and provides reusable utilities for EDA, data cleaning, and embedding. It features modular flows for easy debugging and a FastAPI layer for quick deployments. Essential components like DocHandler and BaseEmbedder ensure efficient data ingestion and embedding, while the Shraga-UI provides a ready-to-use chat interface. This setup enables fast prototyping and production-ready GenAI systems.