Generative AI
Generative AI tutorials, LLMs, ChatGPT, AI agents, RAG, prompt engineering, and practical GenAI guides.
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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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LangGraph Evaluation: 7 Powerful Langfuse Tests
LangGraph evaluation is the process of testing whether a LangGraph agent gives correct answers, uses the right tools, follows the expected graph path, and stays reliable in production. This guide adds Langfuse tracing and evaluation details so you can monitor real agent behavior, not just final text. Start with a small agent and an explicit…
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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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LangGraph vs CrewAI: Which Should You Learn First? (2026 Guide)
Short answer: learn CrewAI first if you want results this weekend, LangGraph first if you want depth that pays off for months If you are starting out with AI agents in 2026, the LangGraph vs CrewAI debate is probably the first real decision you will face. Both are excellent Python frameworks for building multi-agent systems,…
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Claude Opus 5.5 vs GPT-6 Astra: 7 Powerful Comparisons
Updated September 23, 2026 Claude Opus 5.5 arrived on September 22, 2026, and Anthropic is making an unusually aggressive claim for the new model: better performance than Opus 5 on demanding coding and knowledge-work tasks while costing less to run. That immediately raises the comparison developers actually care about in September 2026: Claude Opus 5.5…
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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…
