The Tool Desk
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How RAG lets a local AI use your documents
Microsoft Learn describes the distinction simply: “Retrieval-augmented generation lets you make your data available to LLMs without training them on it first.” RAG makes documents available at inference time, when a question is asked, rather than changing what the model learned during training.
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- Extract text. The system reads the files. PDFs and word-processing documents may need parsing or conversion before their contents can be searched.
- Split text into chunks. Long documents are divided into smaller passages so the system can retrieve relevant sections rather than pass an entire collection to the model.
- Build a searchable index. An embedding model can turn passages into numerical representations. The user’s query is then compared with indexed passages to find potentially relevant material.
- Keep source information. The index stores searchable data and, ideally, metadata or links that connect each passage to its original file. That makes it possible to inspect where an answer came from.
- Retrieve and answer. When you ask a question, the system selects likely relevant passages and sends them along with the question to the model. The model uses that context, along with its general learned capabilities, to generate a response.
Microsoft Learn documents this workflow and explains the value of metadata linking retrieved material back to its source: Retrieval-augmented generation (RAG) provides LLM knowledge.
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Running the language model on your computer does not, by itself, make the entire document workflow local. Documents may pass through other services during extraction, embedding, retrieval, or storage. For an end-to-end local setup, the components that handle your data must also run locally or on infrastructure you control.
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- Document extraction: Check whether file parsing happens on your device or uses an external service.
- Embeddings and retrieval: Confirm that the embedding model and search system run locally if documents and queries must not leave your machine.
- Reranking, if used: A reranker may be an additional service in the pipeline. Check where it runs.
- Storage: A vector store may be in memory, persisted locally, self-hosted, or managed by a provider. Its location and terms matter.
- Generation and telemetry: Verify whether the model sends prompts to a hosted API and whether the application transmits usage or diagnostic data.
LlamaIndex documents a local configuration using a local model runtime, locally run embeddings, an optional local reranker, and an in-memory or self-hosted vector store. Its example says the embedding, reranking, and retrieval steps make no outbound network calls. By contrast, its default tutorials use hosted APIs for generation and embedding; managed vector stores also store embeddings under the provider’s terms. See LlamaIndex’s privacy and security documentation.
Why “local AI” is a configuration claim
A setup can use a local generator while sending queries or document data to hosted components. The word “local” is therefore meaningful only when you know which parts of the pipeline handle the files, passages, queries, and embeddings. Before relying on a setup for sensitive material, check the configuration and the terms for every external provider involved.
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How reliable are document-based answers?
RAG gives the model evidence to work with, but it does not ensure that the system retrieves the right passage or that the model represents it accurately. Document parsing, chunk size, search settings, and source metadata all influence what context reaches the model.
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- Check the original file before acting on consequential claims.
- Be alert to missing context, especially when an answer depends on a definition, exception, or passage spread across multiple sections.
There is no universal accuracy figure established for RAG in the documentation cited here. Results depend on the documents and how the system is configured, so treat source references as a way to verify an answer, not proof that it is correct.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
What affects setup and hardware needs?
There is no single hardware minimum established for running local document RAG. Requirements depend on the selected language and embedding models, the size of the document collection, and the workload. Compare specific setups by checking whether every component remains local, which file formats and extraction paths they support, whether they preserve source references, how much setup and maintenance they require, and the hardware demands of the models and corpus you plan to use.
A local vector store can be kept in memory or persisted to disk. Additional storage may be useful for source files, downloaded models, or a persisted index, but it is not a requirement for RAG and does not improve answer accuracy by itself.
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