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...