Prompt Engineering & AI Application Design Learning Journey
From beginner to capable AI workflow designer who can build, test, and deploy LLM-powered features that solve real business problems.
🎯 Goal
Become a Prompt Engineer / AI Application Designer capable of designing, testing, and deploying reliable AI-powered workflows, tools, and features using large language models and related AI systems, to solve concrete business problems in modern organizations.
Skills to acquire
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Learn to craft effective prompts for generation, extraction, reasoning, classification, and tool‑using agents. Practice structured iteration: A/B testing prompts, changing instructions, format, examples, and constraints to improve accuracy, robustness, and usability across different tasks and domains.
Suggested course: Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs
Provider: Whizlabs
Understanding LLM Capabilities and Limitations
Develop a mental model for what LLMs can and cannot do: hallucinations, reasoning limits, token/context constraints, latency, cost trade‑offs, and robustness issues. Learn to decide when an LLM is appropriate and when traditional software, rules, or search are better options.
Suggested course: Large Language Models
Provider: H2O.ai
Evaluation and Benchmarking of AI Outputs
Learn how to define quality criteria (accuracy, usefulness, safety, style), build golden datasets, create rubrics, and categorize errors. Gain skills in human and automated evaluation, prompt regression testing, and building small evaluation harnesses to track quality over time.
Suggested course: GitHub: Evaluating and Integrating AI Models
Provider: Pragmatic AI Labs
Task Decomposition and Workflow Design
Practice breaking messy business problems into sequenced, LLM‑friendly subtasks (e.g., understand → retrieve → reason → draft → review). Learn to design multi‑step workflows and agentic flows that improve reliability compared with a single all‑in‑one prompt.
Suggested course: AI Workflow: AI in Production
Provider: IBM
Programming and LLM API Integration (Python/JavaScript)
Acquire basic coding skills to call LLM APIs, handle inputs/outputs, manage retries, and log interactions. Learn to integrate prompts into backend or frontend applications and to build simple dashboards for monitoring usage, latency, and quality.
Suggested course: Building Generative AI-Powered Applications with Python
Provider: IBM
LLM Orchestration Frameworks (LangChain, LlamaIndex, etc.)
Learn to use orchestration frameworks to chain prompts, tools, and retrieval steps. Understand how to define agents, tools, memory, and evaluation flows using higher‑level abstractions that speed up prototyping and make complex workflows maintainable.
Suggested course: LLM Application Engineering and Development Certification
Provider: Simplilearn
Retrieval-Augmented Generation (RAG) Fundamentals
Understand how to build systems that ground LLMs in external data. Learn document chunking strategies, embeddings, vector databases, retrieval strategies, and how to combine retrieved context with instructions in prompts to reduce hallucinations.
Suggested course: Vector Databases for RAG: An Introduction
Provider: IBM
Safety, Compliance, and Guardrails for AI Systems
Learn to design prompts and workflows that minimize harmful, biased, or non‑compliant outputs. Implement guardrails such as content filters, validation layers, escalation to humans, and data handling practices aligned with privacy and regulatory requirements.
Suggested course: Responsible AI for Developers: Privacy & Safety - Polski
Provider: Google Cloud
UX Design for AI-Powered Features
Develop skills to design user interfaces and interactions where AI is understandable and controllable. Learn how to set expectations, show examples, surface system limitations, provide controls like regenerate/edit, and design feedback loops for continuous improvement.
Suggested course: Crafting User-First Product Experiences in the AI Era
Provider: Microsoft
Domain Discovery and Problem Framing for AI Use Cases
Learn techniques to work with stakeholders to identify high‑value AI opportunities, translate vague business needs into well‑scoped AI tasks, and gather enough domain knowledge (e.g., marketing, support, analytics) to judge whether outputs are truly useful.
Suggested course: AI Product Management
Provider: Duke University
Collaboration, Documentation, and Communication
Develop the ability to collaborate with engineers, PMs, designers, and subject‑matter experts. Learn to document prompts, workflows, assumptions, evaluation methods, and known failure modes so systems can be maintained and improved by teams.
Suggested course: Technical Communication in the AI Era
Provider: Minnesota State University, Mankato
Continuous Learning and Experimentation in AI
Build habits and lightweight processes for staying current with new models, tools, and techniques. Learn to design small experiments, track metrics and logs, interpret results, and share insights, embracing fast iteration and uncertainty as part of the job.
Suggested course: Advanced Deployment, MLOps, and Generative AI in Azure
Provider: Packt