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Learning Roadmap: Prompt Engineering
Topic: Prompt EngineeringLevel: intermediateFormat: text
Prompt engineering is the practice of designing, testing, and refining instructions for large language models so they produce more accurate, useful, and reliable outputs. At an intermediate level, the goal is not just learning basic prompting patterns, but understanding how to structure prompts for reasoning, extraction, transformation, evaluation, multi-turn conversations, and production-style workflows. Strong prompt engineering also includes iteration, prompt evaluation, guardrails, context management, and awareness that techniques can vary by model family and platform.
The recommendations below were chosen because they are real, currently available, free or free-to-audit, and hosted by reputable providers with strong AI education credibility. Together they balance practical hands-on learning from DeepLearning.AI and Google, enterprise-oriented guidance from Microsoft, platform-neutral conceptual material from Hugging Face, and broader structured courses from Coursera that can be audited for free. This mix is especially good for an intermediate learner because it moves from core prompting principles into reusable workflows, model-specific considerations, and real-world application design.
Prerequisites: You should be comfortable using AI chat tools and understand basic concepts like LLMs, tokens, temperature, hallucinations, and zero-shot vs few-shot prompting. Some courses are accessible without coding, but basic Python or API familiarity will help you get more value from the more technical and developer-oriented materials.
Estimated time: About 18-22 hours total, excluding optional paid labs
Suggested learning path
Start with DeepLearning.AI’s ChatGPT Prompt Engineering for Developers for a fast, hands-on foundation in practical prompt patterns. Next, take Coursera’s Generative AI: Prompt Engineering Basics to expand into more structured prompting strategies like chain-of-thought and tree-of-thought. Then move to Prompt Engineering for LLMs to learn evaluation, long-context handling, and reusable prompt systems. After that, study Google’s Prompt Design in Vertex AI and Microsoft Learn’s prompt engineering lesson to see how major platforms frame prompting in production environments. Finish with the Hugging Face prompt engineering guide to deepen your understanding of model-specific behavior, open-source workflows, and transferable prompting techniques across ecosystems.
Recommended free courses (6)
ChatGPT Prompt Engineering for Developers
DeepLearning.AI
A highly practical short course taught by Isa Fulford and Andrew Ng that covers prompt engineering principles, iterative refinement, summarization, inference, transformation, expansion, and chatbot building. It is best for learners who already know the basics of LLMs and want a concise but hands-on introduction to prompt design patterns used in real applications.
Topics: prompt engineering principles, iterative prompting, summarization, classification, chatbot design, LLM applications
intermediate1h 30mFree4.9/5
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Generative AI: Prompt Engineering Basics
Coursera
This structured course introduces prompt engineering best practices and specific techniques such as interview pattern, chain-of-thought, and tree-of-thought approaches. It is a strong bridge from beginner to intermediate because it emphasizes prompt strategy and reliability rather than only tool-specific demos.
Topics: prompt design, chain-of-thought, tree-of-thought, LLM reliability, best practices
intermediate9 hoursFree4.8/5
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Prompt Engineering for LLMs
Coursera
A more applied course focused on designing reusable prompt structures, handling long context, improving multi-turn interactions, and evaluating prompt quality systematically. It is especially useful for intermediate learners who want to move beyond ad hoc prompting into testable, scalable prompt workflows.
Topics: few-shot prompting, role prompting, context management, prompt evaluation, production workflows
intermediate1 weekFree4.6/5
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Prompt Design in Vertex AI
Google Cloud Skills Boost
This Google course teaches prompt engineering with Gemini in Vertex AI, including concise prompting, specificity, reducing output variability, and using examples effectively. The course materials are available for free, while labs may require credits, so it works well for learners who want Google’s practical framework without necessarily needing paid lab access.
Topics: Gemini prompting, prompt specificity, few-shot examples, output control, multimodal prompting
intermediate1 hour 45 minutesFree4.7/5
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Understanding Prompt Engineering Fundamentals (Part 4 of Generative AI for Beginners)
Microsoft Learn
A free Microsoft lesson in its Generative AI for Beginners series that explains prompt engineering foundations in a clear, developer-friendly way. It is valuable for intermediate learners because it complements conceptual understanding with Microsoft’s broader learning ecosystem and practical model usage context.
Topics: prompt fundamentals, instruction design, few-shot prompting, LLM behavior
intermediate1-2 hoursFree4.6/5
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Prompt engineering
Hugging Face
This free Hugging Face guide is documentation-style learning rather than a traditional video course, but it is one of the best technical resources for intermediate learners. It covers prompt engineering best practices, zero-shot and few-shot prompting, and model-aware considerations that matter when working across open models.
Topics: zero-shot prompting, few-shot prompting, open-source LLMs, prompt best practices, model selection
intermediate2-3 hoursFree4.7/5
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