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Unlocking the Secrets of Prompt Engineering
Duration
3 Days
Course
PP-DSGE-102
Available Formats
Classroom Training, Online Training
Course Description
Overview
Unlocking the Secrets of Prompt Engineering is your key to mastering the art of AI-driven writing. This course propels you into the world of large language models (LLMs), empowering you to create and apply prompts effectively for diverse applications, from revolutionizing content creation and chatbots to coding assistance. Starting with the fundamentals of prompt engineering, this guide provides a solid foundation in LLM prompts, their components, and applications. Through practical examples and use cases, you'll discover how LLMs can be used for generating product descriptions, personalized emails, social media posts, and even creative writing projects like fiction and poetry. The course covers advanced use cases such as creating and promoting podcasts, integrating LLMs with other tools, and using AI for chatbot development. But that’s not all. You'll also delve into the ethical considerations, best practices, and limitations of using LLM prompts as you experiment and optimize your approach for best results. By the end of this course, you'll have unlocked the full potential of AI in writing and content creation to generate ideas, overcome writer's block, boost productivity, and improve communication skills.Objectives
Topics
- Technical requirements
- Introducing LLM prompts
- How LLM prompts work
- Types of LLM prompts
- Components of an LLM prompt
- Adopt any persona – role prompting for tailored interactions
- Finding your voice – defining personality in prompts
- Using patterns to enhance prompt effectiveness
- Mix and match – strategic combinations for enhanced prompts
- Exploring LLM parameters
- How to approach prompt engineering (experimentation)
- The challenges and limitations of using LLM prompts
- Using AI for copywriting
- Creating social media posts
- Writing video scripts
- Generating blog posts, articles and news
- Creating engaging content with AI
- How to use AI for personalized messaging
- Creating tailored content with AI
- Crafting podcast questions for celebrity guests
- Preparing podcast questions with everyday guests
- Identify topics, ideas, and potential guest speakers for your podcast
- Using AI to promote a podcast
- Identifying insightful interview questions
- Sharpening interview skills with AI-generated responses
- Generating strategic questions for client engagements with AI
- Using AI for creative writing
- Using AI to generate fiction
- Using AI to write poetry
- Sentiment analysis – AI techniques for emotion detection in text
- Organizing unstructured data – using AI for automated text categorization and data classification
- Cleaning up dirty data – how AI identifies and resolves issues in datasets
- Making sense of unstructured data – pattern matching for information extraction
- Creating course materials with ChatGPT
- Creating handouts and other materials
- Creating quizzes
- Creating rubrics
- Creating cloze comprehension tests
- AI for legal research
- Reviewing legal documents using an LLM
- Drafting legal documents with an LLM
- AI for legal education and training
- LLMs for eDiscovery and litigation support
- AI for intellectual property (IP) management
- Other applications of LLMs for lawyers
- Code generation with coding assistants
- From confusion to clarity – AI explains what code does in plain English
- Commenting, formatting, and optimizing code
- Fixing faulty code – how AI transforms the debugging process
- Translating code from one language to another
- Case study 1 – developing a website code using AI
- Case study 2 – creating a Chrome extension using AI
- Technical requirements
- How to use GPT-4 APIs and other LLM APIs to create chatbots
- Building conversational interfaces with LLM APIs
- How to use AI for customer support
- Case study – a chatbot using AI to assist users in ordering products
- Case study – creating interactive quizzes/assessments and deploying them as chatbot flows
- Automating bulk prompting with spreadsheets
- Integrating LLMs into your tech stack using Zapier and Make
- Moving beyond APIs – building custom LLM pipelines with LangChain
- The future of LLM integration – plugins, agents, assistants, GPTs, and multimodal models
- Exploring the ethical challenges of generative AI
- Economic impact considerations
- Environmental sustainability issues
- Societal risks and reflections
- The path forward – solutions and safeguards
- Recap of the course content
- Expanding possibilities – innovative prompt engineering applications
- Achieving intended outcomes – prompt engineering goals
- Understanding limitations and maintaining oversight
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- Standalone learning or supplemental reinforcement.
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