Tech to Transform

Understanding Prompts: Components, Types & Characteristics Post 2 – Introduction to Prompt Engineering Series Recap from the Previous Post In our first post  , we kicked off the series by introducing Prompt Engineering — the art and science of crafting effective instructions to guide AI models like LLMs. We saw how clear, structured prompts can significantly influence the quality of the responses. Real examples from Gemma 1B showed how even a small change in phrasing or context can completely shift the model’s output. Now that we know why prompts matter, let’s explore what makes up a good prompt and how different styles and strategies come into play. Today’s Focus: The Building Blocks of Prompts In this post, we’ll cover three key areas that every prompt engineer (or GenAI user) should understand: Components of Prompts – What elements make a prompt effective? Types of Prompts – How can prompts vary based on goal or technique? Characteristics of Good Prom...

A Note of Thanks First off, a heartfelt thank you to everyone who has been following my blog over the past few weeks. I'm thrilled to share that within just 7 weeks, we've crossed 1,000 readers , and the thoughtful comments I’ve received have been incredibly motivating. Your support truly fuels this journey. This blog has unexpectedly become a learning accelerator for me. Before writing each post, I dive deep— reading not just to solve, but to truly learn . In many ways, it feels like being back in college again, where learning was the goal, not just the means. A special mention to my daughter Veena , who creates the beautiful line art illustrations you've seen across these posts. She’s in 12th grade and an art enthusiast with her own Instagram channel. While AI isn’t her subject, our weekly 30-minute collaboration sessions have become a precious bonding ritual for both of us. It’s a reminder that when diverse minds collaborate — one rooted in technology, the other in ...

Building a 100% Free On-Prem RAG System with Open Source LLMs, Embeddings, Pinecone, and n8n After the last post on building a financial statement analyzer using OpenAI and n8n, many readers reached out with a common question: “Can I build a similar RAG system without relying on OpenAI APIs or paid cloud services?” The answer is — yes, absolutely. In this tutorial, I’ll walk you through building a complete Retrieval-Augmented Generation (RAG) system entirely on-prem , using free and open-source tools . No API keys, no vendor lock-in, and no code required. With the help of: n8n for orchestrating your workflow Pinecone as a vector database (free-tier available) Ollama for running open-source LLMs and embedding models locally Windows Command Prompt for setup and automation You’ll create a fully functional RAG pipeline that: Accepts documents Converts them to embeddings Stores and retrieves relevant context Answers user queries intelligently — all fro...