Tech to Transform

Recap from Previous Posts In our earlier posts, we covered the foundations of prompt engineering , including the components of a prompt, their characteristics , and three key prompting strategies — Zero-Shot, One-Shot, and Few-Shot . We also introduced the idea of in-context learning , which enables large language models (LLMs) to learn from examples provided within the prompt , without retraining. Today, let’s take the next step and explore some powerful advanced techniques that go beyond basic input-output interactions. These help unlock more accurate, context-aware, and insightful responses from LLMs, particularly useful in complex, high-stakes domains like banking and risk management . Chain-of-Thought Prompting Definition: Chain-of-Thought (CoT) prompting is a technique that encourages the model to break down its response into a series of logical steps , mimicking how humans approach complex problems. Instead of giving a direct answer, the model walks through the intermediat...

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...