Local AI with Ollama + Qwen3: RAG, Agents & Vector Stores
Production local AI on your own hardware: Ollama + Qwen3, ChromaDB RAG, tool-calling agents, quantization, and security. Runnable code, zero cloud.
Production local AI on your own hardware: Ollama + Qwen3, ChromaDB RAG, tool-calling agents, quantization, and security. Runnable code, zero cloud.
LM Studio runs open-source LLMs locally on Windows, Mac (Apple Silicon), and Linux. Setup, GPU (CUDA/Metal/Vulkan/ROCm), model picks, and RAG in one guide.
Build a robust RAG system end to end: chunking, embeddings, vector stores, hybrid retrieval, reranking, and eval harnesses you actually need in production.
RAG optimization: chunk sizing, hybrid retrieval, reranking, query rewriting, and evaluation — smarter retrieval-augmented systems that actually rank well.
The future of LLMs and fine-tuning: LoRA, adapters, RAG, synthetic data, and the modular techniques replacing full retraining in 2026 production workflows.
Fix common RAG failures: bad chunking, irrelevant embeddings, outdated data, and ambiguous queries. Diagnostic steps, retrieval evals, and patches that work.
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