System Design
System design tutorials, architecture patterns, scalable applications, distributed systems, cloud architecture, and technical interview preparation.
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Llama Guard vs NeMo Guardrails: 7 Key Differences, Architecture, and a Complete Python Project
Llama Guard vs NeMo Guardrails is a comparison that becomes important the moment an LLM application starts handling real users, private documents, or business operations. You have probably seen a familiar pattern in RAG tutorials: load documents, create embeddings, retrieve chunks, send them to an LLM, and display the generated answer. The application works beautifully…
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NeMo Guardrails with LangChain: Build a Safer RAG Chatbot Using OpenRouter
NeMo Guardrails helps developers add programmable safety and behavioral controls to large language model applications. Rather than relying only on a system prompt, NeMo Guardrails lets teams configure checks before a request reaches the model and before its answer is returned. Those controls make NeMo Guardrails useful when a chatbot must follow policies consistently. This…
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RAG Evaluation Dataset Template: 30 Questions to Find Retrieval Failures
Use this 30-question RAG evaluation dataset template to check whether your retriever finds the evidence an answer needs. It includes a fictional source corpus, expected answers, stable question IDs, and a copyable CSV so you can run the exercise before adapting it to your own documents. Editorial update: October 27, 2026. Added a complete fictional…
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nanoMoE: Implementing Mixture-of-Experts LLMs from Scratch in PyTorch
Table of Contents Conclusion FAQs Primary MoE References Introduction Research on large language models (LLMs) has advanced at an extraordinary pace over the past several years. Yet, despite the rapid evolution of the field, the core architecture underlying most LLMs—the decoder-only Transformer—has remained remarkably consistent. More recently, however, a different architectural paradigm has begun to…
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LLM as a Judge: Langfuse Setup, Best Practices & MT-Bench Lessons
LLM as a judge is one of the most practical ways to evaluate open-ended AI outputs when exact-match tests are too narrow. This guide shows how to build, test, and trust an LLM as a judge workflow with Langfuse, MT-Bench lessons, bias checks, and production-ready rubrics. LLM as a Judge: 9 Powerful Practices for Reliable…
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DeepSeek MLA: 4 Attention Methods Compared
DeepSeek MLA: 4 Attention Methods Compared DeepSeek MLA is the focus of this practical guide to MHA, GQA and MQA because its architecture shows how KV cache compression moves from theory into real LLM inference systems. DeepSeek MLA practical checklist Use this quick checklist when deciding whether DeepSeek MLA is the right mental model for…
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Multi-Head Latent Attention (MLA) Explained: DeepSeek Architecture, KV Cache, Math & PyTorch Code
Multi-Head Latent Attention (MLA): How DeepSeek Reduces the KV Cache Run a transformer with a short prompt and the attention mechanism may not look particularly problematic. Increase the context to tens of thousands of tokens, serve several users simultaneously, and generate tokens autoregressively, however, and a different bottleneck appears: the KV cache. Traditional Multi-Head Attention…
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How to Cut LLM API Costs by 80%: Caching, Routing and Context Budgets
Most teams discover their inference bill the way you discover a leak: through the invoice. A prototype that costs a fraction of a cent per call feels free, right up to the point where it is running a hundred thousand times a day and someone in finance wants a word. The good news is that…
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LLM System Design Interview: 15 Questions, Answers & a Framework That Works (2026)
There is a specific way people fail this interview, and it has almost nothing to do with how much they know about machine learning. The interviewer says: “Design a customer support assistant powered by an LLM.” The candidate draws a box labelled LLM, connects it to a database, and then spends thirty minutes explaining what…