From brainstorming business proposals to composing that perfect email, modern writers need more than just a flair for words—they need the right tools to get ahead. After the resounding success of our first workshop, we are delighted to bring back our immersive workshop with Author and Director, Scratch Content Sapna Gupta with a focus on enhancing everyday writing and business communication.
In this second edition, we will dive deeper into the incredible potential of Generative AI (GenAI) as your co-writer. From helping structure clear communications to generating new ideas with ease, GenAI opens doors to more effective and engaging writing. With real-world examples, Sapna will demonstrate how AI can elevate your style, refine your voice, and even save valuable time—whether it is for emails, presentations, or reports.
Join us for an insightful session and master the tools to communicate your ideas with clarity and creativity.
Workshop Highlights:
Learn to integrate Generative AI seamlessly into your writing process to support everyday tasks, from brainstorming to drafting.
Develop skills for adapting your tone across different business contexts resonating with diverse audiences.
Discover AI-powered techniques to refine and polish your drafts for precision and professionalism.
Utilize AI insights to understand your audience's preferences, guiding your content strategy for emails, reports, and social media posts.
Equip yourself with customized AI solutions to save time and enhance productivity.
Sapna Gupta Director, Scratch Content | Author of Staged | Visiting Faculty Whistling Woods | Interest areas - Sustainability, Web 3.0, Generative AI & Prompt EngineeringNamed the ‘Creative Entrepreneur of the Year’ at the Women Entrepreneurship Conclave and Awards 2022, Sapna is a writer, entrepreneur, and generative AI enthusiast. She is the Author of Staged - a book on the live entertainment industry of India. With over a decade and a half of domain expertise in live experiences and creating medium-agnostic communication, Sapna believes in journeying with brands and consumers. Currently at the helm of an award-winning start-up, Scratch Content, she has worked on content strategy and execution for some of the biggest brands in India and overseas.Born in Mumbai, Sapna has spent her formative years in Kolkata before moving back to Mumbai to pursue her passion for content and communication. Sapna is currently working closely on social initiatives and sustainability, alongside speaking, teaching and running Scratch Content.
Read moreWhat is Generative AI?
Generative AI encompasses a group of algorithms capable of creating new, realistic content, such as text, images, and audio, using foundation models as a basis. These foundation models are large neural networks pre-trained on vast amounts of raw, unstructured, and unlabeled data. Training foundation models involve extensive deep learning processes, where practitioners expose algorithms to diverse datasets to capture underlying patterns, relationships, and structures.
The result is a complex neural network containing numerous parameters, essentially encoded knowledge from the training data. With this groundwork laid, these foundation models can be adapted for specific purposes by using smaller, task-oriented datasets in a process known as fine-tuning. This fine-tuning enables the application of generative AI in various contexts, including language generation, image creation, and audio synthesis.
However, generative AI is not without risks. Outputs from these models can appear accurate and credible while containing misinformation or reflecting societal biases. Moreover, they can be exploited for unethical or illegal activities. Ensuring the ethical use and reliability of these models requires careful training and ongoing monitoring. Addressing these risks is key to harnessing the potential of generative AI while mitigating its drawbacks.
What are Large Language Models?
Large language models (LLMs) are sophisticated deep-learning algorithms designed to comprehend and generate human language. They consist of intricate neural networks, typically comprising an encoder and a decoder, that can analyze text, recognize patterns, and produce coherent and contextually appropriate responses. By leveraging extensive pre-training on vast datasets, these models are capable of understanding a wide range of language-related tasks, from generating text and classifying content to answering questions and creating code.
LLMs have found applications across various domains, from copywriting and knowledge base management to chatbots and virtual assistants. These models are also used for research support, content creation, and language translation, with well-known examples including OpenAI's ChatGPT-3, Meta’s Llama, Google’s Gemini, Cohere’s Command model, and LightOn’s Paradigm. As natural language processing technology continues to advance, LLMs are expected to play a pivotal role in driving innovation and shaping future AI applications.
What is Prompt Engineering?
As generative AI models gain traction, the art and science of prompt engineering have become indispensable. This discipline revolves around creating well-thought-out inputs, known as "prompts," to guide AI tools toward desired outcomes. Prompt engineering blends logical reasoning, coding, and creativity, enabling skilled engineers to extract the best from AI systems. These experts analyze and synthesize text, interact with users via chatbots, automate robotic processes, and even create 3D models and robot instructions.
Skilled prompt engineers understand both the potential and limitations of AI models, tailoring prompts to highlight their strengths while mitigating weaknesses. By carefully designing prompts, engineers can help AI systems achieve more accurate, efficient, and creative results. Ultimately, effective prompt engineering empowers generative AI to tackle a broader range of tasks with enhanced precision and versatility.
