Understanding Context and RAG Before talking about RAG, it’s important to understand context in large language models (LLMs).When you send a prompt, the LLM first breaks it into tokens — small units of text, roughly 3–4 characters of English on average. These tokens, along with any chat history and documents you provide, make up the […]
Why RAG is not just for “teaching” LLMs Read More »
Training a neural network is how we teach machines to recognize patterns — like identifying faces, translating languages, or recommending products. It’s the core of how modern AI systems learn from data and improve over time. Learning how to train a network unlocks powerful tools for solving real-world problems, and it’s more approachable than you
How to Train Your Neural Network Read More »
In today’s world of AI innovation, having the power to run large language models (LLMs) locally is becoming increasingly valuable. Whether you’re a researcher, developer, or AI enthusiast, bringing your AI work home means taking control of your environment, data privacy, and workflow efficiency without relying on cloud services. With advancements in GPU technology and
Bringing Your AI Work Home — Setting Up for Local LLMs Read More »
The field of AI Agent Engineering is exploding with opportunity. The good news is that the tools and knowledge you need are now available to everyone, regardless of background. This post gives you a roadmap and curated resources to help you launch your journey with focus and momentum. AI has been fully democratized. This means
Prerequisites for AI Agent Engineer Read More »
If you’ve ever needed to label your data for downstream AI, you know how tedious and error-prone manual tagging can be. Fortunately, with the help of Python and NLP tools like spaCy, you can automate this process and even train a model that understands your domain. In this workshop, you’ll build a system that classifies
