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How to build a basic RAG pipeline for a local LLM

Wire up retrieval-augmented generation from scratch: chunk your documents, embed them, store the vectors, and feed the right context into a local model so it answers from your own data. No cloud required.

Ahmad J · Aug 10, 2026 · 4 min read

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Guide · aiDeep read

The best vector databases for RAG in 2026

Qdrant is still the safe default for new RAG projects in 2026, but the field moved on cost: Milvus 2.6 shipped 1-bit quantization, Pinecone launched Nexus to public preview, and object-storage-first newcomers Turbopuffer and LanceDB undercut the incumbents. An honest buyer's guide.

Ahmad J · Aug 1, 2026 · 12 min read

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Article · aiDeep read

RAG vs Fine-Tuning: Which One Your Use Case Actually Needs

RAG and fine-tuning solve different problems, knowledge versus behavior, and picking the wrong one wastes money and ships worse results. A practical decision guide with an interactive picker, including why a million-token context window does not retire retrieval.

Ahmad J · Jul 27, 2026 · 10 min read

Stack of printed papers and an external hard drive sit on a wooden desk beside a sunlit window with a dark wall behind

Article · ai

What RAG actually is and is not

RAG does not teach a model new facts. It fetches relevant text and pastes it into the prompt, so the model answers from documents instead of memory.

Ahmad J · May 10, 2026 · 4 min read