Welcome to my personal knowledge repository for AI Engineering. This repository contains detailed notes, conceptual guides, tutorials, and code implementations for various topics in modern AI and software engineering.
Here is an overview of the topics covered in this repository:
- 🧠 Large Language Models (LLMs): Architecture, embeddings, context windows, and tokenization.
- ✍️ Prompt Engineering: Verbalized Sampling (VS), Static vs Dynamic Prompts, system prompts, zero-shot/few-shot learning, Chain of Thought (CoT), ReAct pattern.
- 🔍 Retrieval-Augmented Generation (RAG): BM25F Query Search Algorithm, Keyword-Augmented Retrieval (KAR), document loaders, text splitters, vector databases, search/retrieval strategies, and re-ranking.
- 🤖 Agentic Workflows: MCP and A2A, tool use, function calling, state management, and multi-agent frameworks (LangGraph, Autogen, CrewAI).
- 🦜 LangChain: Indexes in LangChain, LangChain Runnables, LangChain Expression Language (LCEL), chains, agents, memory.
- ⚙️ Fine-Tuning & Quantization: LoRA, QLoRA, model evaluation, and optimization.
- 📦 MLOps & Deployment: API serving (FastAPI, Ollama), model registry, monitoring, and scaling.
To read or edit files in this repository locally:
# Clone the repository
git clone https://github.com/niteshkumarvarma30/AI-Engineering-Docs.git
# Navigate to the repository
cd AI-Engineering-Docs- Master advanced RAG techniques (hybrid search, semantic chunking)
- Implement multi-agent collaborative systems
- Fine-tune a small open-source model on custom domain data
- Build and deploy production-ready AI applications
Created and maintained with ❤️ by niteshkumarvarma30.