Fine-Tuning and Model Customization: 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
Prompting versus RAG versus fine-tuning
Instruction datasets
Supervised fine-tuning
LoRA and QLoRA concepts
Dataset quality and evaluation
Deployment considerations
Understand the foundations
Prompting changes context for a request, RAG supplies external evidence and fine-tuning updates model parameters using examples. Choose based on the problem: frequently changing company policies are usually an evidence-management problem, while a stable response style or specialized task pattern may justify customization. Fine-tuning does not guarantee current facts or permission-aware recall and should not become a way to embed confidential user records into a model without a deliberate data policy.
Connect the ideas
Supervised fine-tuning learns from input-output pairs or conversations. LoRA learns low-rank adapter updates rather than modifying all base weights directly. QLoRA combines quantized base-model training techniques with low-rank adaptation; actual compatibility and memory use depend on the implementation and hardware. Create a dataset card covering provenance, rights, consent, redaction, deduplication and intended use. Split by source or scenario to reduce leakage, and reserve a held-out evaluation set before editing examples.
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