Advanced Prompting Techniques (Part 2) | ReAct, Self-Consistency, Graph of Thought & More

Описание: In this Part 2 of my Prompt Engineering series, I continue from the previous video where we covered Zero-Shot, One-Shot, Few-Shot, Chain of Thought (CoT), and Tree of Thought (ToT).

In this video, we take things one level deeper and explore advanced prompting techniques that help large language models reason better, plan smarter, and produce more reliable outputs — all explained at a high level with intuitive examples.

🔍 Prompting Techniques Covered:
Graph of Thought (GoT) – reasoning using interconnected ideas instead of linear steps
Step-Back Prompting – zooming out before solving complex problems
Self-Consistency – improving answer reliability via multiple reasoning paths
ReAct (Reason + Act) – combining reasoning with actions and tool usage
Least-to-Most Prompting – solving complex tasks by breaking them down incrementally

👨‍💻 Who This Video Is For:
Developers working with LLMs
Backend engineers exploring AI integration
Anyone learning prompt engineering beyond the basics

📌 If you haven’t watched Part 1 yet, I highly recommend starting there before jumping into this video.

📚 Extra Reading & References:
If you’d like to explore these techniques in more depth, check out the following resources:

Microsoft Learn – Prompt Engineering Conceptshttps://learn.microsoft.com/en-us/azu...

Prompting Guide – Comprehensive Prompting Techniqueshttps://www.promptingguide.ai/techniq...

Analytics Vidhya – 17 Prompting Techniques to Supercharge LLMshttps://www.analyticsvidhya.com/blog/...