This space is where I practice, experiment, and explore everything about Large Language Models (LLMs), AI agents, and prompt engineering — building from theory to real-world applications.
This repository complements my journey through the LLM Engineering course, which covers:
- 🤖 Large Language Models (LLMs) — understanding how they work and how to apply them effectively
- 🧩 Prompt Engineering — crafting high-quality prompts for creativity, precision, and control
- 🪄 AI Agents — building autonomous, reasoning-driven systems
- 🔗 LangChain & Tool Integration — connecting models with APIs, data sources, and vector databases
- 🚀 LLM Deployment — serving LLMs in real-world applications
- 🦜 LangChain — building agent workflows and LLM pipelines
- 🤗 Hugging Face Transformers — exploring open-source LLMs
- 💬 OpenAI API — GPT models for generation and reasoning
- 🧮 Python, Pandas, NumPy — for data handling and experimentation
- ⚙️ Streamlit / Gradio — for creating interactive LLM demos
- Understand the core architecture of LLMs
- Master prompt engineering for accuracy and creativity
- Build LLM-powered applications and intelligent agents
- Experiment with RAG (Retrieval-Augmented Generation)
- Design and evaluate multi-agent systems
- Deploy production-grade AI applications
This is a personal learning project, but feedback, suggestions, and collaboration ideas are always welcome!
If you’re also learning LLM engineering or taking the same course, feel free to connect — we can learn together 🤝
- LLM Engineering: Master AI, Large Language Models & Agents — the course that inspired this repo
- The OpenAI, LangChain, and Hugging Face communities
- All the open-source contributors advancing LLM research