Introduction to Generative AI and LLMs

Learn how generative AI and LLMs drive business use cases, from text summarization, rewriting, and information extraction to code generation and translation, with risk–reward prioritization and sales strategy.

Explore how traditional ai differs from gen ai, introduce foundation models, pre-training, fine tuning, and the transformer-based structures that enable llms and text generation.

Customize lms for your use case with prompting, rag, and fine tuning. Explore few-shot prompting, context windows, and prompt engineering to synthesize ideas and rank options.

Explore instruction-based prompting, role-playing, ask for references, chain-of-thought reasoning, few short prompting, react (reasoning and acting), tabular output, respond from context, and compare-and-contrast prompting approaches with LLMs.

Create custom gpts with no code by setting up a project and a system prompt for a high school math tutor, test and share the result.

Explore AWS PartyRock to build AI and machine learning apps and prototypes, log in to access sample apps across categories like Learning and Education, and test a Python learning companion.

Explains the limitations of llms such as knowledge cutoff, hallucinations, and biases, and outlines workarounds like reinforcement learning from human feedback and human-in-the-loop evaluation.

Clarify common misconceptions about LLMs and data usage, and summarize token-based pricing, privacy through local deployment, and how hallucination arises in non-deterministic outputs.

Learn how to build safeguards for generative AI, ensuring fairness, transparency, privacy, security, and ethical use through guardrails, data provenance, and human-in-the-loop verification.

Set up guardrails in Amazon Bedrock via the AWS console and boto3, customize messages and filters, and test with Titan Text to enforce safety and privacy.

Explore why AI projects fail and learn to avoid risks with red flags, ROI metrics, and a risk-reward matrix for selecting high-impact, scalable use cases.

Explore generative AI platforms, apps, and use cases from content creation and coding to multi-modal tasks and AI agents, enabling smarter, scalable business solutions.

Discover how llms pair with agents to reason, break complex tasks into steps, and orchestrate actions using external tools and environments.

See how GitHub Copilot analyzes an AWS architecture diagram, explains its components and data flow, and generates ready-to-run HTML/CSS/JavaScript for a video player. The demo notes testing before production.

Explore image and video generation with Amazon Nova, including Nova Canvas and Nova Reel, using prompts, aspect ratios, and customizable settings to create rich, downloadable media for ads and blogs.

Learn to run LLMs locally on your laptop with Olama, download LLAMA2, and view training-based responses including quantum computing knowledge.

Explore how generative AI reshapes search by turning results into AI-generated explanations and chat-driven interactions, shifting from link-based results to solution-oriented responses.

Explore how generative AI and LLMs reshape careers and productivity, uncover market potential, and map out certifications, skills, and practical steps to start today.

Discover a short bonus lecture from Connected World outlining their training, software development, and consulting in artificial intelligence and machine learning, with contact options for your next project.