Prompt Engineering: concepts and vocabulary | 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.
Learning goals
Prompt structure and role instructions
Zero-shot, one-shot and few-shot examples
Structured JSON outputs
Reusable prompt templates
Prompt injection and defensive design
Prompt testing and evaluation
Understand the foundations
A useful prompt states the task, permitted evidence, output contract and behavior when evidence is insufficient. Zero-shot supplies instructions without worked examples, one-shot supplies one example and few-shot supplies several. Examples should cover realistic variation rather than only easy cases. Separate application instructions from user documents and clearly mark the documents as data. Delimiters improve clarity but do not create a reliable security boundary against malicious instructions contained in retrieved text.
Connect the ideas
Treat a prompt template as a versioned application component. A resume-analysis contract might require evidenced_skills, missing_evidence and suggestions, with evidence spans for every extracted item. Parse and validate model output on the backend; a request for JSON does not guarantee schema conformance. Enforce permissions, allowed tools and quotas outside the prompt. Test injection attempts, conflicting evidence, missing fields and overlong inputs alongside ordinary cases. Compare template versions against the same held-out cases before replacing one.
Checkpoint: explain these concepts in your own words, then describe one failure that the design must detect. Use synthetic or permissioned data throughout the course.
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