Tech to Transform

Exploring Zero-Shot, One-Shot, and Few-Shot Prompting Strategies with Real-World Banking Risk Examples In the previous post, we explored the components and characteristics of good prompts — how elements like instruction, context, tone, and format come together to shape the quality of AI responses. Today, we’ll dive deeper into three foundational prompt strategies : Zero- Shot, One- Shot, and Few- Shot Prompting — and understand when, why, and how to use each of them effectively. But before we do that, let’s understand a key concept that powers all three strategies: In- Context Learning . When working with large language models (LLMs), there are typically two major approaches to make them perform well on specific tasks: fine-tuning and in-context learning . Fine-tuning involves modifying the internal weights of a pre-trained model using task-specific data. This process requires large datasets, considerable computing power, and time. It’s a more permanent and resource-intens...

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...