Local LLM accuracy boosted from 1/10 to 9/10 with RAG implementation

The author details their experience implementing Retrieval-Augmented Generation (RAG) to improve the accuracy of a locally hosted LLM, OzAI, running on a MacBook Pro M1 Pro. Initially, the LLM, a Qwen3 8B model served via Ollama and LiteLLM, provided incorrect and confidently fabricated answers. By integrating RAG, which involves searching relevant documents before generating a response, the LLM's performance on a set of 10 evaluation questions improved from 1 out of 10 correct to 9 out of 10. The author emphasizes the importance of including source document headings within RAG chunks and using specific instructions for embedding models. AI

IMPACT Improves accuracy of local LLMs by providing external knowledge, reducing confidently incorrect answers.

RANK_REASON The article describes a technical implementation of RAG to improve a local LLM, which is a tool-level improvement.

Read on dev.to — LLM tag →

• Grafana
• Keda
• LangChain
• LiteLLM
• LlamaIndex
• MacBook Pro M1 Pro
• nomic-embed-text
• Ollama
• Open WebUI
• OzAI
• Qwen3_8B
• retrieval-augmented generation

• product
• infra

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