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Retrieval-Augmented Generation (RAG) has become one of the most common approaches for building AI applications that need to work with external or private knowledge. Instead of aski...
Why the Question and the Document Never Quite MatchContinue reading on Medium »
I added a new feature to my blog: a list of related posts at the bottom of each post. I implemented it using embeddings, and this note documents how. I looked at how other content...
How Embeddings WorkContinue reading on Medium »
An embedding is a list of numbers where similar meaning gives similar numbers. Run one locally with Ollama, compare two, and semantic search stops being a buzzword and becomes arit...
What are embeddings? Embeddings are learned numerical representations—dense arrays of floating-point numbers known as vectors—that transform text, images, products, and user querie...
Short answer: start a private fintech knowledge-base feature with embeddings, in-app retrieval, and grounded chat completions; keep reranking optional until real questions show tha...
Enterprise content management is experiencing its biggest architectural shift since the cloud migration era. For years, enterprises have stored trillions of gigabytes of critical...
Background: Clinical retrieval-augmented generation depends on embedding models. A companion study found that non–retrieval-trained encoders underperformed retrieval-trained genera...
The Uncomfortable Truth You’ve spent days prompt-engineering your LLM. You’ve benchmarked Claude against GPT. You’ve debated whether to use Mixtral. But your RAG pipeline is still...
The demo always works. You paste a PDF into a notebook, split it into chunks, embed them with a hosted model, push the vectors into an index, and ask a question. The answer comes b...
Turning the key principles and methodological stages of GraphEval into a simulated practical scenario to better understand its usefulness and key implications in understanding and...
Welcome to Day 5 of 60. We spent the first four days building the mathematical engine of neural networks. Now, we face a fundamental data…Continue reading on Medium »
Every now and then I build a demo for a presentation and think to myself - this deserves its own blog post. I then promptly forget to actually do that. Even better, I completely fo...
Retrieval quality in an AI search product is bounded by two things: how good the embedding model is, and how cheaply you can run it across an index. This week, Perplexity Engineeri...
Jaydeep Chakrabarty of Piramal Finance on why retrieval alone caps your AI stack, and how a knowledge graph can derive context nobody ever wrote down.
A developer-friendly deep dive into vectors, semantic similarity, and how modern AI systems retrieve meaning instead of simply matching…Continue reading on Artificial Intelligence...
NVIDIA released Nemotron 3 Embed on July 15 and 16, 2026. The collection has three open checkpoints: Nemotron-3-Embed-8B-BF16, Nemotron-3-Embed-1B-BF16, and Nemotron-3-Embed-1B-NVF...
Here's a dirty secret of search: "the closest match" and "the most useful result" are not the same thing. Return the mathematically nearest document and you'll often hand someone s...
Chunking decides more of your retrieval quality than the model does, and the obvious way to measure it rewards the worst possible answer. Here are eight strategies, measured on the...
There is a variable called slp — sea-level pressure. Our API documentation lists it as available. Our error messages list it among the valid options. Our fine-tuned model, asked ab...
Enterprise Document Intelligence [Vol.1 #14B] - No shared fields means no index to build. One summary line per file plus each file’s own table of contents, and retrieval routes dow...
jina-embeddings-v4 is a self-hosted server for the jina-embeddings-v4 embedding model with an OpenAI-compatible /v1/embeddings endpoint. It runs on a single NVIDIA GPU. An applicat...
Group interactions are everywhere in the real world. Researchers co-author papers, shoppers buy products together, students hang out in clusters between classes, and users on quest...
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