This Advanced Prompt Engineering course is designed for developers, data scientists, AI enthusiasts, and professionals who want to master the art of crafting effective prompts for large language models (LLMs) like OpenAI's GPT, Claude, and Gemini. With a strong focus on hands-on practice, the course blends theoretical concepts with real-world applications, guiding learners through the nuances of prompt design, optimization techniques, chaining prompts, and building complex workflows using LLMs. Whether you're developing chatbots, writing AI-assisted content, automating tasks, or enhancing productivity tools, this course equips you with the skills to leverage LLMs more efficiently and creatively.
You will learn the principles and strategies behind prompt engineering, including zero-shot, few-shot, and chain-of-thought prompting, instruction tuning, and role-based prompting. You’ll gain hands-on experience with designing, testing, and refining prompts for different use cases such as code generation, data extraction, summarization, translation, reasoning, and more. The course also covers prompt evaluation, mitigation of hallucinations, integrating prompts with APIs, and using tools like LangChain and semantic search to build intelligent systems.
Get introduced to the world of large language models and understand the critical role prompts play in shaping outputs. Explore real-world use cases across industries and learn the evolution of prompt engineering as a discipline. This module sets the foundation for advanced techniques by covering key concepts and limitations of LLMs.
Dive into the different types of prompting strategies like zero-shot, one-shot, few-shot, and chain-of-thought. Learn how each approach impacts model behavior and when to use which method. Practice designing prompts using these techniques with real-time feedback to understand their strengths and weaknesses.
Learn to write high-quality prompts that are clear, specific, and structured for consistent output. Understand how wording, formatting, and token limitations affect LLM responses. This module includes exercises on rephrasing, decomposing tasks, and using delimiters to isolate instructions or data.
Understand how assigning roles or personas (e.g., “You are an expert data scientist”) changes the tone and accuracy of model responses. Learn the impact of framing tasks as step-by-step instructions versus questions. Practice designing prompts for teaching, consulting, and creative roles.
Discover techniques to assess the quality, consistency, and usefulness of LLM outputs. Explore both qualitative and quantitative evaluation methods, including scoring rubrics, human feedback, and tool-based analysis. Learn iterative refinement to enhance prompts based on test results and context.
Learn how to maintain context and coherence across multiple interactions with the model. Understand token limits, context windows, and memory simulation strategies. Practice designing conversations that build on previous answers for dynamic and intelligent multi-step interactions.
Go beyond single prompts and build workflows where multiple prompts work together to achieve a larger goal. Learn how to chain prompts for tasks like classification followed by generation or reasoning followed by summarization. Use tools like LangChain to manage chaining in real-world apps.
Understand why LLMs hallucinate or produce biased results, and how prompt design can help mitigate these issues. Learn techniques such as grounding, context anchoring, and multi-prompt verification. Explore responsible AI practices in prompt engineering for ethical deployment.
Explore practical tools and environments that streamline the prompt engineering workflow. Learn to use LangChain for chaining, PromptLayer for version control, and vector databases for retrieval-augmented generation. Set up reproducible experiments in Jupyter and cloud environments.
Apply all the learned techniques in hands-on projects across domains like customer support, software development, legal summarization, and more. Work on mini-capstone projects to build a portfolio of prompt-engineered solutions. Receive feedback and optimization suggestions from mentors.