Llm

Modern generative AI applications—especially those built on large language models (LLMs) and Retrieval-Augmented Generation (RAG)—can become chaotic very quickly if they’re not organized well. Multiple model providers, complex prompt flows, vector databases, embeddings, caching, inference orchestration, and deployment considerations all compete for space in your codebase. Without a clear structure, your project becomes difficult to extend, debug, or hand off to other engineers. This article walks through a practical and scalable project structure for a generative AI application: ...

Introduction Small Language Models (sLLMs) are quickly becoming the workhorses of practical AI applications. While frontier models (with hundreds of billions of parameters) grab headlines, small models in the 1B–15B parameter range often deliver better latency, lower cost, easier deployment, and stronger privacy—especially when fine‑tuned for a specific use case. This tutorial is a step‑by‑step, implementation‑oriented guide to working with sLLMs: What sLLMs are and why they matter How to choose the right model for your use case Setting up your environment and hardware Running inference with a small LLM Prompting and system design specific to sLLMs Fine‑tuning a small LLM with Low‑Rank Adaptation (LoRA) Quantization and optimization for constrained hardware Evaluation strategies and monitoring Deployment patterns (local, cloud, on‑device) Safety, governance, and risk considerations Curated learning resources and model hubs at the end All code examples use Python and popular open‑source tools like Hugging Face Transformers and PEFT. ...

Table of Contents Introduction Probability Fundamentals Conditional Probability and the Chain Rule Probability Distributions How LLMs Use Probability From Theory to Practice Common Misconceptions Conclusion Resources Introduction If you’ve ever wondered how ChatGPT, Claude, or other large language models generate coherent text that seems almost human-like, the answer lies in mathematics—specifically, probability theory. While the internal mechanics of these models involve complex neural networks and billions of parameters, at their core, they operate on a surprisingly elegant principle: predicting the next word by calculating probabilities. ...

Table of Contents Introduction Understanding the Foundations Setting Up Your Django Project Integrating LLM Models with Django Building Views and API Endpoints Database Design for LLM Applications Frontend Integration with HTMX Advanced Patterns and Best Practices Scaling and Performance Optimization Deployment to Production Resources and Further Learning Introduction Building web applications that leverage Large Language Models (LLMs) has become increasingly accessible to Django developers. Whether you’re creating an AI-powered chatbot, content generation tool, or intelligent assistant, Django provides a robust framework for integrating LLMs into production applications. ...

Why Most RAG Systems Fail Most Retrieval-Augmented Generation (RAG) systems do not fail because of the LLM. They fail because of bad chunking. If your retrieval results feel: Random Hallucinated Incomplete Loosely related to the query Then your embedding model and vector database are probably fine. Your chunking strategy is the real bottleneck. Chunking determines what the model is allowed to know. If the chunks are wrong, retrieval quality collapses — no matter how good the LLM is. ...