Tech to Transform

  "The Smart Shift: AI in Project Management" In the hustle and bustle of today’s business world, I know how vital it is for project managers like us to deliver results swiftly and efficiently. Traditional methods can sometimes feel a bit limited when tackling the complexities of modern projects—be it managing resources, mitigating risks, or keeping everyone in the loop. That’s where Artificial Intelligence comes into play! It’s truly a game changer. By incorporating AI into our workflows, we can automate those routine tasks that eat up our time, while also gaining predictive insights that help us make informed decisions faster and with greater accuracy. Whether it’s intelligent scheduling or analyzing performance metrics, AI has the potential to transform how we manage our projects and collaborate within our teams.

  Defensive Prompt Engineering Protecting Prompts Against Attacks, Manipulations & Information Leaks As I’ve discussed in earlier blog posts, large language models (LLMs) are getting smarter, faster, and more capable . Many of the initial limitations and challenges we explored—like contextual confusion or narrow understanding—are steadily being mitigated in newer and more advanced models. However, as LLMs evolve , so do the threats and risks associated with their use. Just as prompt engineering has matured into a critical skill for maximizing output quality, defensive prompt engineering is now essential for ensuring safety, compliance, and intellectual property protection. Today, LLMs are increasingly embedded in business-critical workflows — especially in high-stakes domains like finance, legal, healthcare, and cybersecurity . That means their exposure to prompt extraction, injection attacks, and misuse has grown — making robust defensive techniques more relevant than ev...

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...

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...