Series · 8 parts
Applied LLMs — Origins to Production
A read-in-order path through large language models as they actually get used: the historical arc, the mechanics of attention, retrieval augmentation, prompting that holds up, and applied case studies. Start at the origins and walk forward.
- 1Where LLMs Came From, and What They Actually AreThe 80-year origin story, the five ideas that actually matter, and a verified watch-list to go deeper — no maths required.
- 2How Transformers actually attendBeyond the textbook diagrams — what a single attention head is really computing, how multi-head splits the world, and why scaling laws keep rewarding bigger context.
- 3RAG architecture basics and workingsRetrieval-Augmented Generation from first principles — embeddings, vector databases, chunking, retrieval, prompt construction, evaluation, and common failure modes — enough depth to build one yourself.
- 4Best LLM Prompt for understanding any concept in-depthUse this prompt for gathering information all at one place
- 5Automating audio generationFrom text , generate audio files and publishing them to webapp
- 6Reading on MEDICAL ERROR DETECTION AND CORRECTION IN CLINICAL NOTESThis blog explores a paper on detecting and correcting medical errors in clinical notes using Large Language Models (LLMs)
- 7Stanford CS229: Machine Learning CourseCS229 provides a broad introduction to statistical machine learning (at an intermediate / advanced level) and covers supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical ); and reinforcement learning among other topics
- 8Basics of Azure AI FoundryA self-sufficient guide to Azure AI Foundry — what it is, how the hub/project/deployment model works, how to ship a grounded agent end-to-end with SDK + Bicep, and the security/eval/cost levers you cannot skip.