Generative AI Essentials 2 Expertise: Complete Concise Guide

Master advanced prompting techniques: chain of thought, tree of thought, and self-reflection, to enable multi-step reasoning, plus multimodal prompting for images, videos, and insights.

Continue advancing prompt engineering by self-learning from internet sources, using the base prompt, refining with technique hints, and leveraging creativity to craft the best prompts.

Explore practical prompt engineering techniques using ChatGPT and Gemini, including zero-shot, one-shot, few-shot prompts, templates, chaining, and blogger prompts, with hands-on examples.

Master advanced prompting techniques, including chain of thought and tree of thought, with multimodal prompts using Gemini and ChatGPT, and generate test data for quick test cases.

Fine-tune a generic language model with a custom question-and-answer dataset in Colab using the Gemini 1.5 base model, then test and tailor a domain-specific chatbot.

Assess key security risks in gen ai systems, including data breaches, adversarial attacks, model theft, and misuse, then implement encryption, access controls, and audits to build trustworthy, transparent systems.

Embed ethics into AI development to prevent bias and unaccountable decisions. Apply fairness checks, privacy safeguards, and transparent governance to build trust.

Explore why explainable AI matters for trust, debugging, fairness, and accountability in high-stakes domains, and learn techniques like attention visualization and counterfactual explanations.

Start with prompt engineering to add context and instructions, then use Rag to bring in up-to-date data, and finally fine-tune the model for style and domain nuances to boost performance.