Generative AI APIs and Application Development: 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
API concepts and server-side authentication
REST integration
Streaming responses
Tool/function calling
Structured output validation
Errors, retries and rate limits
Token usage and cost monitoring
Understand the foundations
An AI API is a remote dependency with authentication, latency, failure modes and usage limits. Keep provider credentials behind the backend and authenticate your own users independently. Define a stable internal request and response contract so the UI does not depend on a provider-specific body. Tool calling is a request from the model to invoke a named operation; it is not permission to execute arbitrary code. Validate tool arguments and current user access before any operation.
Connect the ideas
Set connection and request deadlines, bound input and output sizes and distinguish retriable failures from invalid input. Use capped exponential backoff with jitter for selected transient failures, respect retry guidance and avoid retrying a side effect without an idempotency strategy. Streaming may split a UTF-8 character or JSON fragment across chunks. Use an incremental decoder and a defined event protocol, with cancellation and an explicit end or error event. Do not parse each network chunk as a complete message.
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