Production

Introduction Retrieval‑augmented generation (RAG) has become a cornerstone of modern AI applications—from enterprise knowledge bases to conversational agents. At its core, RAG combines a retriever (often a vector similarity search) with a generator (typically a large language model) to produce answers grounded in external data. While the concept is elegant, deploying RAG in production demands more than just functional correctness. Real‑time user experiences, cost constraints, and operational reliability force engineers to optimize every millisecond of latency. ...

FastAPI’s speed, async capabilities, and automatic API documentation make it ideal for building production-grade APIs serving Large Language Models (LLMs). This guide details best practices for deploying scalable, secure FastAPI applications handling LLM inference, streaming responses, and high-throughput requests.[1][3][5] LLM APIs often face unique challenges: high memory usage, long inference times, streaming outputs, and massive payloads. We’ll cover project structure, async optimization, security, deployment, and LLM-specific patterns like token streaming and caching. ...

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. ...

Introduction Claude Code introduces a powerful feature called Skills—a way to teach Claude repeatable, specialized capabilities that persist across sessions. Think of Skills as plugins for behavior: structured instruction sets that define exactly what Claude should do, when to do it, and which tools it can use. Unlike one-off prompts that you type into chat, Skills are persistent, discoverable, and automatically selected by Claude based on context. They transform Claude from a general-purpose assistant into a specialized agent that can reliably perform complex, domain-specific tasks. ...

Unsloth has quickly become one of the most practical ways to fine‑tune large language models (LLMs) efficiently on modest GPUs. It wraps popular open‑source models (like Llama, Mistral, Gemma, Phi) and optimizes training with techniques such as QLoRA, gradient checkpointing, and fused kernels—often cutting memory use by 50–60% and speeding up training significantly. This guide walks you from zero to production: Understanding what Unsloth is and when to use it Setting up your environment Preparing your dataset for instruction tuning Loading and configuring a base model with Unsloth Fine‑tuning with LoRA/QLoRA step by step Evaluating the model Exporting and deploying to production (vLLM, Hugging Face, etc.) Practical tips and traps to avoid All examples use Python and the Hugging Face ecosystem. ...