Tech GPT

When working with software requirements or testing documents in a CSV format , crafting detailed queries or instructions using prompt engineering becomes crucial. These documents often have structured data (e.g., requirements, test cases, priorities) that require targeted processing, such as extracting specific information, analyzing gaps, or generating additional data like test scenarios. In this blog, we’ll explore how to write detailed queries and instructions to process software requirements or testing documents stored in CSV format using prompt engineering . Understanding the CSV Structure Before writing prompts, it’s essential to understand the structure of the CSV file . Here’s an example of how software requirements or testing data might look in a CSV: Example CSV Data: requirements.csv ID,Requirement,Type,Priority,Status 1,Users must be able to register and log in using their email and password,Functional,High,Approved 2,Search functionality must return relevant results wi...

In this guide, we'll walk through the full process of reading data from a CSV file , converting it into JSON , using prompt engineering to query that JSON data, retrieving a single-row answer , and finally storing the result back into another CSV file. This workflow combines data processing and the power of Generative AI to interact with tabular data in a human-friendly way. Overview of Steps Read the CSV file and convert it into JSON format. Use prompt engineering to query the JSON data. Retrieve a single-row answer based on the user’s query. Append the result as a new row to another CSV file. Step 1: Read the CSV File and Convert It into JSON Before querying the data, we’ll start by reading the data from the CSV file and converting it into a JSON format that the AI model can interpret. Sample CSV File ( data.csv ) Here’s an example of the data we’ll use: Name,Age,Department,Salary Alice,30,HR,50000 Bob,45,IT,70000 Charlie,28,Finance,60000 Diana,35,Marketing,65000 ...

Prompt engineering is at the heart of leveraging Generative AI models effectively. Whether you're working with tools like GPT, Claude, or foundation models on AWS Bedrock, mastering prompt engineering can make the difference between mediocre and outstanding results. As an intermediate prompt engineer, you likely already know the basics—crafting clear instructions, testing outputs, and iterating on prompts. Now, it's time to level up. This blog explores advanced guidelines for prompt engineering , offering strategies, techniques, and best practices to help you optimize your prompts and achieve more consistent, nuanced, and effective outputs. 1. Think Like the AI: Context is King Why Context Matters AI models rely heavily on the context you provide in your prompt. They don’t "think" like humans—they predict the next word or token based on the input. Including relevant information in your prompt can help the model generate more accurate and targeted responses. Best...

 Artificial Intelligence (AI), particularly Generative AI , has revolutionized the way we interact with technology. From chatbots and content generation to code assistance and creative outputs, models like OpenAI’s GPT, Google’s Bard, and Amazon’s Bedrock foundation models are capable of performing incredible tasks. A key part of using these models effectively is prompt engineering , which involves crafting prompts (or instructions) to generate the desired outputs. However, what many beginners overlook is the role of inference parameters —special settings that can fine-tune how the AI responds. Understanding these parameters can take your results from "okay" to "amazing." In this blog, we’ll break down inference parameters in prompt engineering and explain how to use them to improve AI-generated results. What Are Inference Parameters? Inference parameters are settings that control how an AI model generates outputs when given a prompt. These parameters influence...

AWS Bedrock enables businesses and developers to harness the power of foundation models for Generative AI. While AWS Serverless in Bedrock provides a robust infrastructure for building and deploying custom AI applications, AWS Marketplace offers distinct advantages in certain contexts—especially when it comes to accessing domain-specific Large Language Models (LLMs), pre-built solutions, and seamless integration with Amazon SageMaker. This blog explores the advantages of AWS Marketplace over a serverless approach in Bedrock, with a focus on domain-specific LLMs and how SageMaker enhances Marketplace tools. 1. Access to Domain-Specific Large Language Models (LLMs) Serverless in Bedrock AWS Bedrock provides access to foundation models from providers like Anthropic, Stability AI, and AI21 Labs. While these models are powerful for general-purpose tasks (e.g., text generation, summarization), they may lack specialization in certain industries or domains. AWS Marketplace Advantag...

The world of Artificial Intelligence (AI) is evolving rapidly, and one of the most exciting developments is Generative AI —a type of AI that can create text, images, code, and more. If you’ve ever used tools like ChatGPT or DALL·E, you’ve experienced the magic of Generative AI. But how do businesses and developers harness this power to build their own applications? Enter AWS Bedrock , Amazon Web Services’ new platform for Generative AI. In this blog, we’ll break down what AWS Bedrock is, how it works, and why it’s a game-changer for anyone looking to leverage Generative AI—even if you’re a beginner. What is AWS Bedrock? AWS Bedrock is a fully managed service that makes it easy for developers to build and scale applications powered by Generative AI. The platform provides access to pre-trained AI models from some of the world’s leading model providers, so you don’t have to worry about creating and training your own models from scratch. Think of AWS Bedrock as a bridge between comp...

Artificial Intelligence (AI) is rapidly transforming industries, creating new opportunities and challenges. Among the most exciting and impactful areas is AI Ethics and Responsible AI . As organizations increasingly rely on AI for decision-making, ensuring that these systems are ethical, fair, and transparent has become a critical priority. This has opened up career paths, research areas, and entrepreneurial opportunities for those interested in shaping the future of responsible AI. In this blog, we’ll explore the vast opportunities available in AI Ethics and Responsible AI, and how you can get involved. Why is AI Ethics Important? AI systems are embedded in many aspects of our lives—healthcare, hiring, law enforcement, education, finance, and more. With this widespread adoption, ethical concerns are growing: Bias in AI : AI systems can inherit biases from training data, leading to unfair outcomes. Lack of Transparency : Many AI models act like "black boxes," making d...