Search: “rag”

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Executive Summary Since the release of our May 2026 report detailing adversarial misuse of artificial intelligence (AI), Google Threat Intelligence Group (GTIG) has observed forward leaning adversarie

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arXiv:2610.09375v1 Announce Type: new Abstract: Organizations increasingly use frontier language models to analyze customer feedback, but answer quality also depends on how that feedback is organized

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Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios u

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Didn't expect this 🤯 We replaced embeddings with Jev in GPT Researcher's RAG pipeline and tested both on 28 research tasks from SimpleQA and open ended research. Jev beat embeddings on every quality m

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Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrie

I maintain a starter kit where the LLM provider is a runtime-swappable DB row, not a hardcoded constant — OpenAI, Anthropic, or a local Ollama instance. Mostly so I can demo without burning API credit

Wanted to share a project I've been working on to give local Ollama models persistent document memory without needing heavy vector databases. It's called Hillock. The main problem I had with standard

Disclosure: I am the creator of this project. After days of lurking and building up enough karma, I can finally post here. Every time I tried running local RAG on my own machine, I hit the exact same

arXiv:2610.02510v1 Announce Type: new Abstract: Campus AI tutors based on retrieval-augmented generation (RAG) must ground answers in assigned course materials while keeping textbooks and student dial

Wanted to share a local memory engine I built called Hillock. The main problem I had with standard local RAG was that running document parsing and vector search chewed up so much VRAM that my actual O

In my attempt to combat powerty; as electricity cost a ton these days and it is hot as hell, I consolidated 2 machines into one, so my 2 crappy machines now are one, and that means a bit less power an

On-device personal AI requires models and agent software that can operate within fixed hardware limits. Developers also need ways to keep those systems improving after deployment. Model builders are r

Disclosure first: I'm one of the maintainers, this is my project. If you've pointed Open WebUI or a script at a local server, you know the failure modes: the server restarts and every conversation is

Executive Summary Since the release of our May 2026 report detailing adversarial misuse of artificial intelligence (AI), Google Threat Intelligence Group (GTIG) has observed forward leaning adversarie

Automated lint: 12 errors, 53990 warnings, 3 info

Automated lint: 12 errors, 54839 warnings, 3 info

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Automated lint: 17 errors, 59669 warnings, 3 info

As threat actors increasingly use AI to accelerate and develop cyberattacks, enterprise defenders need to rely on both AI and a critical defender’s advantage: Business context that only you possess. E

Automated lint: 19 errors, 64915 warnings, 3 info

arXiv:2610.02510v1 Announce Type: new Abstract: Campus AI tutors based on retrieval-augmented generation (RAG) must ground answers in assigned course materials while keeping textbooks and student dial

Automated lint: 14 errors, 54840 warnings, 3 info

I've been comparing and working on Gemma 4, Phi-4-Mini, and Qwen3-4B for local LLM use on an 8 GB Raspberry Pi 5. The results were indeed close, but each model has a different strength: Gemma 4 E2B: a

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As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means

Perplexity AI released a preview of its embedding model “pplx‑embed‑v2‑context‑9b” on Hugging Face on September 30, 2026 (7:07 PM local time). The Hugging Face repository page displays 3 122 views and

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https://huggingface.co/google/embeddinggemma-2 EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combina

EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimens