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The author shares a learning roadmap for RAG and memory systems geared toward Agent development and interviews, covering chunking, hybrid retrieval, reranking, vector database selection, and short-term/long-term memory management, along with reference materials including the original RAG paper by Lewis et al.
A 10,000-word guide sharing the learning path to becoming an Agent engineer, covering LLM fundamentals, Prompt Engineering, RAG, Agent applications, and backend engineering. Includes supporting learning materials, project requirements, and acceptance criteria to help readers systematically fill in their AI application development skills.
A comprehensive, bookmark-worthy roadmap covering software architecture from fundamentals and design principles to distributed systems, DDD, evolution, and communication practices for developers aiming to become architects.
A practitioner shares a 6-month roadmap to become an AI engineer from zero, covering software foundations, LLM app development, RAG, agents and evals, deployment/reliability, and specialization — emphasizing that companies hire people who build systems on top of models, not people who train them.
Agent-Learning-Hub by DataWhale is a systematic learning roadmap and resource repository for AI Agent development, featuring 8 learning stages and 11 progressive projects to help developers build reliable AI agents from scratch. It has gained 3.9k+ stars.
This learning resource called AI-Roadmaps organizes 8 systematic learning paths in AI and data science, covering from basic math to advanced topics, suitable for career changers or beginners.
An open-source learning roadmap project called Agent-Learning-Hub, which breaks down AI Agent learning into 8 stages from building a minimal Agent loop to production deployment, providing executable todo lists and recommended resources, maintained by members of the Datawhale community.