Practical assignment: Create a tested career prompt library | Complete Generative AI: From Foundations to Production
A self-paced 26-week learning path with 15 modules: AI foundations, Python, machine learning, transformers, LLMs, prompts, APIs, embeddings, RAG, frameworks, agents, customization, multimodal AI, Spring Boot with React, and production LLMOps. Includes 15 practical assignments and three portfolio projects.
Prerequisites: Begin with basic computer skills. Programming is introduced in Weeks 2–3. Use a local Python environment for coding labs; Java and React experience helps in Weeks 21–22. Model downloads, provider accounts and deployment resources are optional learner-managed dependencies. The 26-week schedule is suggested; reading estimates exclude independent project work.
Your assignment
Build a reusable prompt library for resume analysis and job-description drafting. Include a purpose, input schema, output schema, version and at least two examples for each template. Add tests for fabricated achievements, empty resumes, multilingual text, document injection and unsupported numeric claims. Evaluate schema validity and evidence support separately from writing style. Preserve failed outputs with synthetic inputs so future changes can be compared. Report prompt improvements using actual case outcomes rather than a subjective claim that a prompt is better.
Build and review
Write the input/output contract and record dependencies or model versions before implementation.
Implement the smallest baseline, then compare the intended AI-assisted behavior on the same fixtures.
Record actual outputs, one ordinary case, one boundary case and one failure case.
Submit code or design notes in Practice; include test evidence, unresolved limitations and next steps.
Use the matching exercise in Practice to save your implementation or a concise repository and evaluation summary. Submissions are stored for self-review, without code execution or instructor grading. Module quizzes check the concepts independently; completing a project does not automatically establish that a deployed system is safe or that its output is correct.
Find this module’s assignment in Learn AI → Practice. The three larger portfolio builds are under Projects. Finish this module’s assessment after reviewing your results.
01. Introduction to Artificial Intelligence · Week 1
02. Python Programming for Gen AI · Weeks 2–3
03. Mathematics and Machine Learning Foundations · Week 4
04. Deep Learning and Transformers · Weeks 5–6
05. Large Language Models (LLMs) · Weeks 7–8
06. Prompt Engineering · Weeks 9–10
07. Generative AI APIs and Application Development · Week 11
08. Embeddings and Vector Databases · Weeks 12–13
09. Retrieval-Augmented Generation (RAG) · Weeks 14–15
10. LangChain, LlamaIndex and AI Frameworks · Week 16
11. AI Agents and Agentic AI · Weeks 17–18
12. Fine-Tuning and Model Customization · Week 19
13. Multimodal Generative AI · Week 20
14. Gen AI with Java Spring Boot and React · Weeks 21–22
15. Production Gen AI, LLMOps and Capstone · Weeks 23–26