#RAG

Detailed instructions for building a complete AI Agent Platform with xClaw — TypeScript monorepo supporting Multi-LLM, RAG Pipeline, Workflow Engine, 13 Domain Packs, Multi-tenant RBAC, MCP Protocol and 8 Chat Channels. From Dual-Database architecture to deploying Docker production.

Duy TranMarch 30, 202645 min

Comprehensive course on AI Agent Engineering — from Python & ML foundation, NLP & LLMs (LLaMA, Mistral, Qwen, Phi), RAG with Vector DB (FAISS, Milvus, Pinecone), to building AI Agent systems with LangChain, LlamaIndex, CrewAI, LangGraph. Practice Fine-tuning, Prompt Engineering, Tool Calling, Multi-Agent. Deploy production with FastAPI, Docker, Microservices, MLOps, CI/CD on AWS/Azure/GCP. Fully meets AI/ML Engineer recruitment requirements at leading technology companies.

Comprehensive study roadmap for NVIDIA DLI Generative AI courses — from Diffusion Models, RAG Agents, Agentic AI to LLM Evaluation & Fine-tuning. 10 in-depth lessons with hands-on code, coding assessment practice, and sample questions closely matching the real exam.

A hands-on series for building a local AI stack with Gemma 4 on Apple Silicon following engineering best practices. From Ollama setup, API integration, RAG pipeline, hybrid retrieval, to observability and hardening for internal environments.

In-depth series on Enterprise AI Chatbot Platform system architecture: multi-model gateway, RAG pipeline, agentic architecture (multi-agent orchestration, tool calling, planning & reflection), conversation memory, streaming & voice, guardrails & safety, multi-channel deployment, multi-tenant architecture, analytics & observability, evaluation & optimization, GPU infrastructure & model serving. Build an enterprise-level chatbot platform from A-Z, ready for production.

A hands-on series on building a complete AI Agent Platform using TypeScript — from monorepo design, LLM Router, Tool Registry, RAG Pipeline, Workflow Engine, Multi-tenant RBAC to deploying Docker production. Learn through the actual source code of xClaw — an open-source platform running in production.

In-depth course on Retrieval-Augmented Generation (RAG) — techniques for connecting the LLM to your own data. From basic RAG to Graph RAG, Agentic RAG, Multimodal RAG. Hands-on with ChromaDB, Qdrant, LangChain, LlamaIndex. Deploy "Chat with Documents" to production.

Comprehensive course on Fine-tuning Large Language Models — when to fine-tune, data preparation, fine-tuning on Google Gemini/Vertex AI, OpenAI, and open-source (LoRA/QLoRA). Compare Fine-tuning vs RAG, model evaluation methods, and production deployment. Calculate actual costs.

Practical course on building AI Agents — from simple chatbots to complex Multi-Agent systems. Proficient in Function Calling, Tool Use, RAG, MCP, LangGraph, CrewAI and deploying Agent to production. Each lesson is coded hands-on with Python.

Comprehensive course on Artificial Intelligence and Large Language Models — from Neural Networks platform, Transformer architecture, to Fine-tuning, RAG, AI Agents and production deployment. Learn hands-on with Python, PyTorch, and LLM APIs.