AI Engineering

The explosion of Large Language Models (LLMs) like GPT-4 and Claude has revolutionized how we build software. However, these models suffer from two major limitations: knowledge cut-offs and “hallucinations.” To build production-ready AI applications, we need a way to provide these models with specific, private, or up-to-date information. This is where Retrieval Augmented Generation (RAG) comes in, and the heart of any RAG system is the Vector Database. In this guide, we will go from zero to hero, exploring the architecture, mathematics, and implementation strategies of vector databases. ...

Introduction Tool calling (also called function calling or plugins) is the capability that turns large language models from text predictors into general-purpose controllers for software. Instead of only generating natural language, an LLM can: Decide when to call a tool (e.g., “get_weather”, “run_sql_query”) Decide which tool to call Construct arguments for that tool Use the result of the tool to continue its reasoning or response This post is a deep dive into the anatomy of tool calling: the moving parts, how they interact, what can go wrong, and how to design reliable systems on top of them. ...

Introduction As language models are woven into more complex systems—multi-tool agents, retrieval-augmented generation, multi-model stacks—“what should handle this request?” becomes a first-class problem. That’s what a semantic router solves. Instead of routing based on keywords or simple rules, a semantic router uses meaning (embeddings, similarity, sometimes LLMs themselves) to decide: Which tool, model, or chain to call Which knowledge base to query Which specialized agent or microservice should own the request This post is a detailed, practical guide to semantic routers: ...

Introduction Designing systems around large language models (LLMs) is not just about calling an API. Once you go beyond toy demos, you face questions like: How do I keep latency under control as usage grows? How do I manage costs when token usage explodes? How do I make results reliable and safe enough for production? How do I deal with context limits, memory, and personalization? How do I choose between hosted APIs and self-hosting? This post is a zero-to-hero guide to system design for LLM-powered applications. It assumes you’re comfortable with web backends / APIs, but not necessarily a deep learning expert. ...

As an expert AI and PyTorch engineer, this comprehensive tutorial takes developers from zero PyTorch knowledge to hero-level proficiency in building, training, fine-tuning, and deploying large language models (LLMs). You’ll discover why PyTorch dominates LLM research, master core concepts, implement practical code examples, and learn production-grade best practices with Hugging Face, DeepSpeed, and Accelerate.[1][5] Why PyTorch Leads LLM Research and Deployment PyTorch is the gold standard for LLM development due to its dynamic computation graph, which enables rapid experimentation—crucial for research where architectures evolve iteratively. Unlike static-graph frameworks, PyTorch’s eager execution mirrors Python’s flexibility, making debugging intuitive and prototyping lightning-fast.[5][6] ...