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.
SwirlAI·1yBuilding Deep Research Agent from scratch
The post guides readers through building a Deep Research Agent using the DeepSeek R1 model. It explains the concept of Deep Research Agents, outlines their components and steps involved, and provides a thorough implementation guide using SambaNova's platform. The setup includes planning the research, splitting tasks, performing in-depth web searches, reflecting on gathered data, and summarizing results into a final research report. The necessary code and prompts are shared for an interactive learning experience.
SwirlAI·51wBreaking into AI Engineering in 2025.
A comprehensive roadmap for becoming an AI Engineer in 2025, covering essential skills from Python fundamentals and LLM APIs to advanced topics like AI agents, RAG systems, and observability. The guide emphasizes learning fundamentals while building practical skills, starting with basic LLM integration and progressing through vector databases, prompt engineering, agentic systems, infrastructure deployment, and security considerations. Key recommendations include mastering FastAPI and Pydantic, understanding different LLM types and structured outputs, implementing RAG with proper data preprocessing, and learning agent design patterns like ReAct and task decomposition.