Jul 6, 2025 · 2 min read · Intro. Finding the perfect icon for your text can be a frustrating experience. You type in keywords, hoping to stumble upon something that fits, but often come up short. In an age where computers understand human language better than ever, shouldn't ...
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Mar 5, 2025 · 9 min read · In the spirit of “Learning in Public,” and “learning exhaust,” I’m adding “Lab Notes” blog posts that chronicle little discoveries or failures that I have along the way to a larger goal. This is a followup to my previous Lab Notes: Comparing GenePT a...
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Jan 20, 2025 · 3 min read · Introduction LLM/Inference LLMs are the "brain" of RAG systems, responsible for interpreting input queries, retrieving relevant knowledge, and generating coherent, context-aware responses. Key roles of LLM in a RA system involve Natural Language Unde...
Join discussionDec 16, 2024 · 23 min read · Introduction to Local RAG What is Retrieval-Augmented Generation (RAG)? The core idea is to enhance the generative capabilities of language models by incorporating external knowledge retrieved from a document store or database. This approach is very ...
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Nov 21, 2024 · 7 min read · Transformers are the backbone of large language models (LLMs) and have revolutionized natural language processing by enabling models to handle vast amounts of text efficiently. Unlike traditional neural networks, transformers use a self-attention mec...
Join discussionJul 30, 2024 · 3 min read · Many people are extremely conversant in AI, but still haven't typed that first command to interact with a real AI model. Let's change that in a single blog post. The first technology you need is called a Jupytr Notebook. There are lots of online host...
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Sep 24, 2023 · 16 min read · Introduction In machine learning, embeddings have emerged as a transformative technique that empowers us to capture intricate patterns from data using dense numerical vector representations. These embeddings encapsulate semantic information from dive...
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