Evaluate when to use prompting, RAG, or fine-tuning by examining model selection trade-offs in cost, latency, and quality. Develop foundational skills by benchmarking candidate models and preparing instruction-tuning datasets. Build practical capabilities by fine-tuning models with LoRA, QLoRA, and adapters on Hugging Face and comparing quality before and after fine-tuning.

Analyze how multimodal LLMs process and combine text, image, and audio inputs. Develop foundational skills by generating image captions and answering questions over images with vision-language models. Build practical capabilities by integrating speech-to-text and text-to-speech into generative AI applications.

Analyze reliability patterns for GenAI services by examining caching, retries, provider fallbacks, and observability. Develop foundational skills by building a GenAI API with FastAPI and containerizing it with Docker. Build practical capabilities by deploying containerized GenAI applications to the cloud.

Evaluate LLMOps practices by examining how versioning, experiment tracking, guardrails, and red-teaming keep GenAI systems safe in production. Develop foundational skills by versioning prompts and outputs and tracking experiments. Build practical capabilities by implementing guardrails and red-teaming applications for toxicity and hallucination.

Evaluate the production readiness of GenAI systems by examining tracing, monitoring, and drift detection. Develop foundational skills by tracing and monitoring deployed applications and detecting model and data drift. Build practical capabilities by designing a deployed, guarded, and monitored end-to-end GenAI application that integrates retrieval, fine-tuning, and LLMOps practices.