Materials for the LLM Engineering Essentials course

Prompt engineering is an essential skill for harnessing the full potential of large language models (LLMs). By carefully crafting prompts, you can guide these powerful AI systems to generate high-quality, relevant, and coherent outputs.

Supporting materials for the LLM Engineering Essentials course

📚 A curated list of essential, classic, and contemporary papers on Generative Artificial Intelligence (GenAI), covering LLMs, diffusion models, RLHF, prompt engineering, and more.

The IBM AI Engineering Professional Certificate on Coursera is a comprehensive program designed to equip learners with the skills necessary to excel in AI and machine learning. This 13-course series covers a wide array of essential AI skills, including machine learning, deep learning, neural networks, and large language models (LLMs)

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This project demonstrate technical abilities in text processing, problem-solving, and prompt engineering skills essential for working with Large Language Models (LLMs).

Prompt engineering is the art of writing prompts to get large language models (LLMs) to do what you want them to do. It is a relatively new discipline, but it is quickly becoming essential for anyone who wants to use LLMs for research or development.

Master Prompt Engineering with Google Cloud Vertex AI and Gemini. This repository covers essential techniques like few-shot prompting, hallucination guardrails, and task classification. A practical Python guide for controlling LLM outputs and building reliable GenAI applications.

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Technical lab for GenAI Efficiency: Bridging low-level systems (C++) with high-level AI agents to optimize inference and resource usage.

Solutions and notes from Nebius Academy LLM Engineering Essentials course

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my learning journey

LLM Engineering Essentials: hands-on workshop

LLM Engineering Essentials course by Nebius Academy

Jupyter notebooks demonstrating essential LLM development concepts environment setup with python-dotenv, caching responses, LLM streaming, and prompt engineering using templates, chat prompts, simple chains, and sequential chains.

An TypeScript monorepo starter (Turborepo) for autonomous LLM-led development. It provides a complete spec-to-production prompt workflow, Docker infrastructure, structured PRDs, and essential shared utilities (JWT auth, crypto, math, logging) to accelerate agent-driven engineering

The Modular Architecture System (MAS): A strategic logic overlay for LLMs. It transforms AI sessions into deterministic, high-integrity workspaces by enforcing strict version control and eliminating memory drift. Essential for engineering, academic research, and complex state management.