Practical AI engineering for engineers and technical professionals — transformer architecture, LLM fine-tuning and inference, multi-agent system design, prompt engineering, AI hardware, and how to apply AI copilots to real engineering work.
Build your own Large Language Model from scratch — no API keys needed. 10 interactive chapters + 6-step wizard covering tokenization, n-gram models, temperature sampling, self-attention, and autoregressive text generation. All computation runs in your browser.
Click-through interactive deep-dive into LLM and Transformer architecture. 17 slides covering self-attention math, scaling laws, LoRA fine-tuning, RLHF alignment, RAG pipelines, KV caching, quantization, production deployment, and the open-source ecosystem.
Visual no-code builder for connecting LLMs to your engineering data, documents, and processes. Build custom AI assistants for spec writing, calculation checking, and report generation.
Curated prompt templates for engineering tasks — code generation, specification drafting, drawing review, RFI writing, and calculation documentation.
Generate Python scripts for common engineering calculations, data processing, and report automation. Unit conversions, load calculations, and chart generation included.
Paste text to estimate token count, track context window usage, and project per-call and monthly API cost using your own model pricing.
AI engineering has no government license — competence is shown through vendor and platform certifications. This is an overview of the certifications that matter for AI/LLM engineers and practitioners building with generative AI, what each covers, who runs it, and how to prepare.
Azure AI Engineer (AI-102) prep: Cognitive Services, vision, NLP, knowledge mining, and Azure OpenAI.
NVIDIA DLI certifications prep: deep learning, accelerated computing (CUDA), computer vision, and LLMs/GenAI.
How EEs use AI for NEC load calculations, protection coordination, PV sizing, arc flash analysis, and construction phase automation.
Deep-dive into transformer architecture, self-attention math, scaling laws, LoRA fine-tuning, RLHF, RAG pipelines, and production inference.
GPUs, TPUs, NPUs, ASICs, CUDA, HBM memory bandwidth, NVLink, cloud pricing, and the full AI software stack from silicon to PyTorch.
From AI-900 to AI Architect — every major cert track with exam costs, study hours, free resources, and a 90-day study plan.
Master prompting for ChatGPT, Claude, and Gemini — zero-shot, few-shot, chain-of-thought, role personas, templates, and AI freelancing.
Build AI agents and automation pipelines with n8n, Make.com, and LangChain. Agent architecture, vector memory, error handling, and monetization.
Complete engineering case study: problem scoping, data pipeline, transformer architecture, pretraining, SFT, instruction tuning, RLHF/DPO, LoRA, RAG, safety evaluation, and production deployment with vLLM.