RAG

Posts that use this term

• Troubleshooting local LLMs and keeping up

  The catalog of common local-LLM failures: OOM, slow tok/s, garbage output, instruction drift, RAG miss, tool-call hallucination. Plus where to follow the field as it moves.

• Fine-tuning a model locally

  When fine-tuning is the right answer (rarely) and how to do it on consumer hardware: LoRA, QLoRA, MLX-LM, Unsloth. A worked example fine-tuning Llama 3.2 3B on a 16GB Mac.

• Local agents and tool use

  Function calling on open models in 2026: which models actually work (Qwen 2.5, Hermes 3, Llama 14B+), why local agents fail when they fail, and how to build defensive scaffolding around them.

• Local RAG and embeddings

  A complete local RAG pipeline in 30 lines: nomic-embed-text for embeddings, Chroma for the vector DB, Llama 3.2 for the chat model. Why local RAG often beats cloud RAG for personal knowledge bases.

• Integrating a local LLM into your workflow

  Wire your local LLM into VS Code (Continue, Cline), web UIs (Open WebUI, LibreChat, Page Assist), and your own apps via the OpenAI-compatible API. The swap-cloud-for-local pattern in real codebases.

• Every machine can run a local LLM (here's what fits)

  Per-tier guide: 8GB integrated graphics, 16GB MacBook Air, 8/12/16/24/32GB VRAM PCs, 24/32/64/128/192GB Macs. Specific models, specific tok/s, specific configs. Every tier runs something useful.

• Picking a local model by task

  The 2026 open leaders by task: coding (Qwen 2.5 Coder, DeepSeek-Coder), chat (Llama, Qwen, Mistral), small-model renaissance (Phi-3, Gemma 2), structured output, multimodal, embeddings.

• The local-LLM vocabulary

  Parameters, B, dense vs MoE, base vs instruct, tokens, context window, chat template, GGUF, quantization suffixes. After this post you can read any HuggingFace model card.

• The pitch for local LLMs in 2026

  Why every engineer should run a local LLM in 2026: privacy, zero marginal cost, lower latency, no rate limits, and offline. Even a 16GB MacBook Air runs Llama 3.2 3B at 30 tok/s.

• Prompt, RAG, fine-tune: three ways to shape a model

  Three levers for shaping what an LLM does: prompting (ask better), RAG (give it the right context), fine-tuning (change the weights). What each costs, what each fixes, and how to pick.

• RAG: giving a model memory it doesn't have

  RAG is the pattern of fetching relevant text from a search system and putting it in the LLM's context window before asking your question. Not magic, not fine-tuning — just better prompts.

• The context window, and why models hallucinate

  An LLM only sees a fixed-size slice of text at a time. When it doesn't know something, it predicts anyway — that's a hallucination, not a bug.