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A "Professional Certificate Course in Prompt Engineering" is a specialized program focused on rapidly designing, developing, or responding to engineering challenges in fields like software, systems engineering, or project management, available through specialized institutions, professional development organizations, or online platforms.
"Indian prompt engineering companies got $ 720 million in funding to date." - (Express Computer Source). Businesses can now make more informed decisions, anticipate problems, and increase the return on investment (ROI) of their marketing campaigns because of prompt engineering. It is anticipated that the Prompt Engineering sector in India would grow to $1.4 billion by 2023.
Prompt engineering is being used by businesses to outperform their competitors. It is predicted that India would win the race in quick engineering. India is home to a large pool of reasonably priced analytical talent with excellent performance potential given the right training. The method that goods are sold has evolved with the rise of digital media. Companies must create items with unique intellectual property rights if they want to stay competitive.
INR 55,000 40,000/-
Prompt Engineering Training Programme Overview
One effective method to mould and enhance language model behaviour is Prompt Engineering. We may affect the output and get more accurate, dependable, and contextually relevant responses by carefully crafting the prompts. We may further improve model productivity and management over produced output by utilising strategies such as CoT prompting, N-shot prompting, and self-consistency. We can fully use language models and open up new avenues for natural language processing if we adopt rapid engineering.
In natural language processing (NLP), prompt engineering has shown to be a powerful technique for enhancing the adaptability and usefulness of language models. We may modify the behaviour & output of these models to accomplish certain goals or elicit targeted reactions by thoughtfully crafting the prompts. We will examine the idea of rapid engineering, its importance, and a range of methods and applications in this extensive book. We will offer guidance & examples to help you fully utilise the possibilities of prompt engineering, from fundamental prompt formatting to sophisticated tactics like N-shot prompting & self-consistency.
What is Prompt Engineering?
The process of developing efficient prompts that allow AI models to provide replies depending on inputs is known as prompt engineering. Writing intelligent prompts for text-based Artificial Intelligence activities, especially Natural Language Processing (NLP) tasks, is known as prompt engineering. These prompts aid in the generation of a specific output as required by the user and the model in the case of such text-based jobs. The moniker Prompt Engineering originates from the effective addition of these needs in the form of prompts.
What is Prompt?
Prompts are brief text passages that provide machine learning models context and direction. Regarding the particular text AI tasks, also known as NLP tasks, these suggestions are helpful in producing pertinent results that closely resemble the intended result. Specifically, these questions aid in producing precise answers by:
- Adding on some further instructions for the model.
- avoiding oversimplifying a prompt.
- Make sure you don't include too much information, since this might confuse the model.
- Clarifying the user's intent and goal will help the model only produce material that makes sense in the given context.
Prompt Engineering Training Learning Outcomes
Prompt Engineering is a growing field that combines computer science, linguistics, psychology, and creativity to create prompts for artificial intelligence systems. With demand growing over 20% annually, India has the potential to become a leader in this field. With over 3.1 million engineers graduating annually from Indian universities and a growing AI ecosystem, India has a skilled engineering workforce ready to meet Prompt Engineering needs. However, India must overcome gaps in specialized AI education and limited advanced research compared to countries like the US and China. Investments in Prompt Engineering include establishing a dedicated educational curriculum, industry partnerships, and policy incentives. By pilot testing and cultivating talent, India can become a leader in Prompt Engineering, driving innovation and securing a competitive edge in technology. This requires a collaborative effort between academia, industry, and government to develop Prompt Engineering from the ground up. India's engineering talent, technology sector growth, and push for advanced AI make it a promising opportunity to emerge as a global hub for Prompt Engineering expertise in the 21st century.
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Who Should Sign Up?
- Data Scientists
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- DevOps Engineers
- IT/Software Engineers
- Machine Learning Architects
- Model Risk Managers/Auditors
- Lecture with a blend of theoretical & practical exposure
- Discussions & final exam to attain the certificate
- Quizzes and group activities
- Internship opportunity for eligible candidates
Prompt Engineering Certification Modules
360DigiTMG provides a comprehensive program for Certified Prompt Engineers, offering resources and courses tailored to Prompt Engineering. With a Premium Subscription starting at $25 per month, users can access a full course library, including Prompt Engineering, AI, and data science, with each course granting a certificate. Acquiring this Prompt Engineering certification opens infinite career opportunities and contributes to shaping the future of AI. It validates skills, makes users resourceful when finding job opportunities, and allows them to lead projects in existing organizations. Joining the ranks of esteemed professionals driving innovation and excellence in Prompt Engineering is a great opportunity to advance in this groundbreaking field.
- Introduction to Prompt Engineering: An overview of prompt engineering, its significance in AI interactions, and the basic principles for crafting effective prompts.
- Foundations of Prompting: Understanding the core concepts behind language models, how they interpret prompts, and the importance of guiding model output through precise prompting.
- Crafting and Refining Prompts with ChatGPT Demonstrations: Practical exercises and demonstrations on creating prompts for various tasks such as information retrieval, creative writing, and more, using ChatGPT.
- Introduction to language models: What are Language Models and understand how these Language Models Work. Learn about various applications of LLMs
- Understanding model responses: Strategies for interpreting the output of language models, challenges, and limitations. Learn the basics of refining prompts to achieve more accurate and relevant responses
- The role of prompts in guiding model output: Explore different types of Prompts, Prompting Strategies, and Principles of effective prompt designing.
- Zero-shot and One-shot Prompting: Techniques for creating prompts that guide the AI in performing tasks without prior examples or with a single example.
- Information Retrieval and Creative Writing: Strategies for extracting information effectively and for generating imaginative content.
- Advanced Prompt Structuring: Exploring context expansion, content summarization, and template filling to achieve specific outcomes.
- Prompt Reframing and Combination: Methods for altering and combining prompts to refine AI responses.
- Interactive Storytelling and Language Translation: Crafting prompts for dynamic storytelling, role-playing, and accurate language translation with contextual nuance.
- Sequential Prompting and Self-Consistency: Developing sequences of prompts for complex tasks and ensuring consistent AI responses.
- Analogical and Meta-Ask Prompting: Using analogies and meta-questions to enhance understanding and consistency.
- ReAct and OPRO: Techniques for reactive, context-aware responses, and optimization prompting for performance improvement.
- Chain-of-Thought and Iterative Prompting: Encouraging the AI to "think aloud" and refining prompts through iteration for clarity and depth.
- Ethical Use and Bias Mitigation: Guidelines for responsible use of language models, addressing biases, and ensuring fair outcomes.
- Privacy and Security: Best practices for maintaining user privacy and security measures to prevent misuse of AI technology.
- Exploring Prompting Tools: Introduction to libraries, models, and frameworks available for prompt engineering, including practical applications and case studies
Trends in Comprehensive Prompt Engineering
The goal of Prompt Engineering, a constantly developing area, is to facilitate effective and efficient human-machine communication. With developments in chatbots, virtual assistants, and natural language processing, quick engineering has advanced significantly as a result of technological breakthroughs.
Advanced Natural Language Processing (NLP)
Natural language processing, or NLP, has made it possible for machines to comprehend, interpret, and produce human language. It is able to analyse voice and text, spot trends, and produce replies that are human-like thanks to deep learning algorithms as well as a large amount of data. Advanced natural language processing (NLP) model GPT-3 from OpenAI produces high-quality text in a variety of styles and tones. It is utilised in chatbots, automated writing, and customer support.
Personalized Prompt Engineering
Personalization is a growing trend in technology, particularly Prompt Engineering. This involves designing systems that adapt to individual user preferences, providing a more tailored experience. Netflix's recommendation system, which uses machine learning algorithms, has been successful, with over 80% of content viewed based on recommendations. More companies are expected to adopt personalized Prompt Engineering for more engaging user experiences.
Multimodal interfaces enable users to interact with machines using various communication methods, such as speech, gestures, touch, and eye tracking. These interfaces make interactions more natural and intuitive, making machines more accessible and user-friendly. For instance, Amazon Echo uses voice commands, touch controls, and gestures to understand user intentions.
Conversational AI, powered by AI chatbots, is gaining popularity in healthcare, customer service, and e-commerce. These systems can engage in natural, human-like conversations, handling routine tasks and providing 24/7 support. The Royal Bank of Scotland's chatbot handles over 50% of customer interactions, showcasing its success. Future advancements will see more advanced AI systems handling complex tasks and providing personalized support.
Edge computing is a local data processing technology that reduces latency and improves response times for real-time applications. Google's Pixel Buds use edge computing in Prompt Engineering, utilizing on-device machine learning for real-time language translation without a constant internet connection, demonstrating its potential in real-time applications. .
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