📘 Modern SOTA Prompt Engineering Techniques

🚀 This repository demonstrates state-of-the-art prompting techniques and their real-time practical implementation using Large Language Models (LLMs). It is designed not just as a notebook — but as a portfolio piece to showcase modern skills in Prompt Engineering, LangChain, and Agentic AI systems.

📌 Project Overview

Prompting is the new programming.

This project explores 10 industry-standard prompting techniques, including:

• Role-based Prompting

• Chain-of-Thought (CoT) Prompting

• Few-Shot Prompting

• Zero-Shot with Implicit Knowledge Prompting

• ReAct (Reason + Act) Prompting

• Self-Consistency Prompting

• Prompt Chaining

• Instruction + Constraint Prompting

• RAG-Enhanced Prompting

• Meta-Prompting (self-reflection)

🛠️ Tech Stack

* Python 3.10+

* LangChain – for prompt orchestration

* OpenAI GPT Models – for LLM responses

* Jupyter Notebook (ipynb) – implementation & walkthrough

🎯 Why This Project Matters

This project bridges the gap between theory and practice. Instead of just definitions, you’ll find working implementations that recruiters, collaborators, and hiring managers can instantly relate to:

• Shows technical expertise in LangChain & LLM orchestration

• Demonstrates awareness of industry trends in Agentic AI

• Provides a foundation to scale towards production-ready AI systems

📬 Connect with Me

👋 I’m passionate about LLMs, RAG, and AI Agent Systems. If you’re a recruiter, engineer, or fellow AI enthusiast, feel free to connect with me:

💼 LinkedIn[https://www.linkedin.com/in/raguwing-gudla/]

🐙 GitHub[https://github.com/Raguwing]

⚡ "Prompting is not just asking questions. It’s the skill of shaping intelligence."

Each technique is demonstrated with structured prompt templates and real-world examples relevant to business, education, and AI agent workflows.