Gen AI seminar prep

Help me talk to these three people on Oct 7th for a seminar on Geneative AI hosted by NVIDIA, here is a brief description of each, I want to know everything about each of them if allowed and also draft a repo for me to work on and include the README.md for the repo which would help in preparing for attending this: https://www.nvidia.com/en-us/events/how-to-prepare-for-nvidia-gen-ai-certification Here is a brief description of the three speakers, do your best research to help with all of this. The only output I want is the repo readme.md which has the high level architecture as well as every single detail needed for the repo. I want it to look professional and be formatted properly to the best of your ability, here are the three descriptions but please also do your own research : Gary Fluitt Technical Certification Manager NVIDIA Gary Fluitt is a technical certification manager with NVIDIA. He launched and ran technical certification programs for Sun Microsystems, Google, and Stripe before joining NVIDIA. Gary has a bachelor’s in instructional design and a master’s in education. He won the IT Certification Council’s Innovation Award in 2023 for his work on Stripe’s performance-based certification for developers, and he’s a frequent speaker at industry events. Aeshah Hadges Certification Manager NVIDIA Aeshah Hadges is the certification program manager at NVIDIA, overseeing the creation and implementation of the developer certification program. Her extensive background includes curriculum development and management of training programs and initiatives aimed at empowering and educating professionals. With an MBA from the University of California, San Diego, she leverages her expertise in business operations and strategic planning to drive NVIDIA’s educational initiatives forward. Behzad Torkian Principal Research Scientist University of South Carolina Behzad Torkian is an expert in AI, large language models (LLMs), high-performance computing, and scalable architectures. He has contributed to multiple patents and developed advanced platforms that bridge cutting-edge research with real-world applications. His work drives innovation in cloud computing and machine learning, enabling organizations to harness AI at scale. Behzad has been collaborating with NVIDIA and bringing his deep technical expertise to help develop new generative AI and agentic AI certifications. He also leads AI strategy across multiple startups, turning emerging research into impactful products and solutions.

Here’s a professional, detailed **README.md** draft for a repo you can use to prepare for the NVIDIA Generative AI Certification seminar on **October 7**. It includes a high-level architecture of prep work, research about the three speakers to help you craft questions or talking points, and all the details you’d need in the repo. You can clone this or adapt. --- ```markdown # Preparation for NVIDIA Generative AI Certification Seminar (Oct 7) ## Table of Contents 1. [Objective](#objective) 2. [Overview of Seminar & Certification Tracks](#overview-of-seminar--certification-tracks) 3. [Speaker Profiles & Insights](#speaker-profiles--insights) 4. [What to Prepare / Prep Architecture](#what-to-prepare--prep-architecture) 5. [Repo Structure](#repo-structure) 6. [High-Level Architecture for Preparation Workflow](#high-level-architecture-for-preparation-workflow) 7. [Tasks & Timeline](#tasks--timeline) 8. [Deliverables](#deliverables) 9. [Resources & References](#resources--references) --- ## Objective To be fully prepared for the NVIDIA Generative AI Certification webinar on **October 7**, and derive maximum value: - Understand the structure, topics, and expectations of NVIDIA’s Generative AI / LLMs / Agentic AI certification exams. citeturn0search4turn0search1 - Be able to ask informed, technical and instructional design-oriented questions, especially relevant to the speakers’ backgrounds. - Build a personal study plan / project (in this repo) that simulates the skills assessed by the certification (coding, architecture, multimodal, agentic AI, etc.). - Produce notes, sample questions, possibly a small project or proof of concept to test knowledge. --- ## Overview of Seminar & Certification Tracks From NVIDIA’s event page: - Seminar Title: *How to Prepare for NVIDIA Generative AI Certification* citeturn0search4 - Date & Time: **October 7**; two sessions (EMEA-APAC & Americas-LATAM) citeturn0search4turn0search1 - Learning Objectives: * Determine which exam is right for you or your team: structure, topics, skill focus. citeturn0search4 * Exam preparation strategies, sample questions. citeturn0search4 * Live Q&A. citeturn0search4 - Certification tracks mentioned: * Associate: Generative AI and LLMs citeturn0search4 * Associate: Multimodal Generative AI citeturn0search4 * NEW! Professional: Agentic AI citeturn0search4 * NEW! Professional: Generative AI LLMs citeturn0search4 --- ## Speaker Profiles & Insights Below are compiled details & interesting insights about each speaker, to help you prepare meaningful questions or identify knowledge gaps. | Speaker | Background & Key Info | Possible Angles / Questions | |---|---|---| | **Gary Fluitt** — Technical Certification Manager, NVIDIA | • Manages technical certification programs at NVIDIA. citeturn0search4turn0search0turn0search2 <br>• Previously launched & ran certification programs at **Sun Microsystems**, **Google**, and **Stripe**. citeturn0search4turn0search0 <br>• Education: Bachelor’s in Instructional Design; Master’s in Education. citeturn0search4turn0search0 <br>• Awarded the IT Certification Council’s Innovation Award in 2023 for work at Stripe (performance-based certification for developers). citeturn0search4 <br>• Frequent speaker at industry events. | • Given his instructional-design background: ask about how the exam design ensures alignment with learning science (e.g. what models are used—competency-based, performance-based, vs multiple-choice). <br>• How he balances technical rigor vs fairness/ accessibility. <br>• What metrics or data NVIDIA collects from past certification takers to improve the exams. <br>• Insights into the design of the new “Agentic AI” exam: what is tested, what's expected in terms of autonomy, decision making, etc. | | **Aeshah Hadges** — Certification Program Manager, NVIDIA | • Oversees creation & implementation of the **developer certification program** at NVIDIA. citeturn0search4turn0search1 <br>• Background strong in **curriculum development** and training program management. citeturn0search4 <br>• MBA from UC San Diego. citeturn0search4 <br>• Strategic planning & operations in educational / professional development settings. | • Ask about how curriculum development practices are integrated into the exam prep materials—how sample questions / labs are developed. <br>• How they ensure international / cross-region consistency (for people from different backgrounds). <br>• How model / exam blueprints evolve (feedback loops). <br>• Business/operational constraints: timelines, tooling, infrastructure used to support candidates (e.g. labs, hands-on). | | **Behzad Torkian** — Principal Research Scientist, University of South Carolina | • Title: Principal Research Scientist, USC. Also listed as leading “Large Language Models & AI” in USC’s Research Computing / IT group. citeturn1search3turn0search1turn0search4 <br>• Has a PhD; did dissertation on “Computational Tools for Analyzing Correlations Between Microbial Biological Diversity and Ecosystems.” This involved tooling / pipelines for metagenomic data. citeturn1search2turn1search0 <br>• His group is concerned with HPC, scalable architectures, LLMs, bridging research & real-world application. citeturn0search4turn0search1turn1search0 <br>• He collaborates with NVIDIA in developing certification content in agents / generative / LLMs. citeturn0search4 | • Technical question angles: what performance / scalability expectations will be part of the cert exams (e.g. latency, memory, deployment at scale). <br>• What kind of coding tasks will be required: is there hands-on labs, model fine-tuning, prompt engineering, evaluation metrics, etc. <br>• How they deal with multimodal and agentic AI from research to cert content. <br>• How reproducibility, bias, ethical considerations are incorporated. <br>• Deployment / infrastructure: what GPU / accelerator types, cloud / edge architectures will candidates need to know. | --- ## What to Prepare / Prep Architecture To make the best use of the seminar and to prepare afterwards, you should build out a preparation workflow/project with components like below: - **Knowledge gathering**: exam blueprints, published sample questions, NVIDIA’s whitepapers, details about “Agentic AI” and “Multimodal Generative AI.” - **Hands-on labs / mini-projects**: small implementation work to test LLMs, multimodal models, agentic agents, prompt engineering, evaluation. - **Practice questions / mock exams**: write your own based on blueprint topics; test speed and accuracy. - **Reflection & feedback loop**: after each mock exam / lab, identify weak areas; revisit reading or hands-on. - **Resource collection**: free courses, papers, code repos, pretrained models. --- ## Repo Structure Here’s how this repo might be organized to support your prep. You can clone locally, or set up a GitHub repo. ``` nvidia-genai‐certification‐prep/ ├── README.md ├── speaker_profiles/ │ ├── gary_fluitt.md │ ├── aeshah_hadges.md │ └── behzad_torkian.md ├── exam_blueprints/ │ ├── associatellm_blueprint.md │ ├── multimodal_blueprint.md │ ├── agentic_ai_blueprint.md │ └── generative_ai_llms_blueprint.md ├── sample_questions/ │ ├── multiple_choice/ │ ├── hands_on/ │ └── scenario-based/ ├── labs/ │ ├── llm_fine_tuning/ │ ├── multimodal_model_experiments/ │ ├── agentic_agent_pipeline/ │ └── prompt_engineering/ ├── mock_exams/ │ ├── mock1/ │ ├── mock2/ │ └── timed_mock/ ├── readings/ │ ├── papers/ │ ├── blogs/ │ └── lectures/ └── reflections/ ├── weak_areas.md └── strategy.md ``` --- ## High-Level Architecture for Preparation Workflow Here’s a diagram of how the components interact. (You might draw this in your head or via a tool; I’ll describe.) ``` [Readings & Blueprints] → [Sample Questions + Labs] → [Mock Exams] → [Analysis & Reflection] → [Refinement (labs, readings)] → [Final Review] Also there’s a feedback channel: Insights from Seminar (Oct 7) → incorporate into Blueprints & Labs. ``` Key modules: - **Blueprint Module**: Collect exam format, topic weights, skills expected (coding, architecture, evaluation, etc.). - **Practice Module**: Practical hands-on experiments & sample questions. - **Mock Exam Module**: Simulate exam conditions. - **Analysis Module**: Measure performance, identify weak spots. - **Improvement Module**: Focused work on weaknesses. --- ## Tasks & Timeline | Date | Task | |---|---| | ASAP (today-within a few days) | Build speaker profiles; collect existing blueprints + exams sample questions; set up repo skeleton. | | 1 week before Oct 7 | Run first mock exam; identify weak topics; prepare targeted labs. | | Day before Oct 7 | Review sample questions, ensure familiarity with exam format; prepare questions for Q&A in seminar. | | Oct 7 Seminar Day | Attend; take notes especially on “new” content (Agentic AI etc.); ask prepared questions. | | After Seminar | Incorporate new info; run final mock; schedule exam or further study accordingly. | --- ## Deliverables - Completed speaker profiles (for your internal prep). - Blueprints for all tracks. - At least two mock exams with your performance metrics. - Lab work: one LLM fine-tuning project; one multimodal experiment; one agentic AI pipeline (if feasible). - Reflection document: what you learned, what to continue improving. - Questions for the speakers (if you attend the live Q&A, or follow up). --- ## Resources & References - NVIDIA Certification event page: *How to Prepare for NVIDIA Generative AI Certification* citeturn0search4 - Speaker info from that page for Gary, Aeshah, Behzad citeturn0search4turn0search1 - USC Research Computing, also bio/dissertation of Behzad Torkian citeturn1search0turn1search2turn1search3 - LinkedIn / public profiles for Gary Fluitt citeturn0search0turn1search1 --- Let me know if you want me to generate sample mock questions tailored to these tracks, or even sample labs. Happy to build those too.

now give me a prompt I can give CLIne to build this for me after I have put the readme.md in a folder

Here’s a solid **initial CLIne prompt** you can drop in after you’ve created a folder and placed the `README.md` inside. It sets full context and asks CLIne to iteratively scaffold the repo for you. --- ```markdown You are CLIne, my coding copilot. Context: I am preparing for the NVIDIA Generative AI Certification Seminar on Oct 7. I’ve already written a `README.md` in this folder with the repo plan, architecture, and prep workflow. Your task is to **read the README.md** and then scaffold the repository based on the structure it specifies. Follow these steps: 1. Parse the `README.md` to extract the repo structure and module breakdown. 2. Create all folders and placeholder files listed (e.g., `speaker_profiles/gary_fluitt.md`, `exam_blueprints/associatellm_blueprint.md`, etc.). 3. Populate each placeholder file with starter content relevant to its purpose: - **Speaker profiles**: add a short bio + placeholder for Q&A notes. - **Exam blueprints**: add headings for “Exam Overview,” “Topics Covered,” “Skills Tested,” and “Resources.” - **Sample questions**: include at least one starter multiple-choice and one scenario-based question as a template. - **Labs**: add a README in each lab folder describing what the lab is for, and include placeholder `.py` or `.ipynb` files (empty, ready to implement). - **Mock exams**: add a simple markdown exam template with instructions and a section for scoring. - **Readings**: add README.md with links to initial suggested resources (you can copy some from the main README). - **Reflections**: create `weak_areas.md` and `strategy.md` with section headers only. 4. Ensure all files are neatly formatted in Markdown (or code where appropriate) with clear headers. 5. Do **not** overwrite the existing `README.md`. Only create the supporting repo structure. 6. After scaffolding, output a clear summary of what you built. Constraints: - Follow the repo structure exactly as listed in the README. - Use best practices for organizing study repos (clean naming, markdown headers, logical starter content). - Do not fill in actual seminar content yet — just scaffolding and starter templates. Now, read the `README.md` and scaffold the repo accordingly. ``` --- Do you want me to also write you a **follow-up CLIne loop prompt** (so it can iteratively add actual content like mock exam questions, lab code, etc.), or just keep it as scaffolding for now?

now write the next prompt to finish the repo this is the output from cline from the previous prompt : I have completed scaffolding the repository structure as specified in the README.md file. All directories and placeholder files have been created with relevant starter content. Here is a summary of the repository structure I created: . ├── README.md ├── exam_blueprints │ ├── agentic_ai_blueprint.md │ ├── associatellm_blueprint.md │ ├── generative_ai_llms_blueprint.md │ └── multimodal_blueprint.md ├── labs │ ├── agentic_agent_pipeline │ │ ├── README.md │ │ └── agent.py │ ├── llm_fine_tuning │ │ ├── README.md │ │ └── fine_tune.py │ ├── multimodal_model_experiments │ │ ├── README.md │ │ └── multimodal_experiments.ipynb │ └── prompt_engineering │ ├── README.md │ └── prompt_engineering.ipynb ├── mock_exams │ ├── mock1 │ │ └── exam.md │ ├── mock2 │ │ └── exam.md │ └── timed_mock │ └── exam.md ├── readings │ ├── README.md │ ├── blogs │ │ └── .gitkeep │ ├── lectures │ │ └── .gitkeep │ └── papers │ └── .gitkeep ├── reflections │ ├── strategy.md │ └── weak_areas.md ├── sample_questions │ ├── hands_on │ │ └── task1.md │ ├── multiple_choice │ │ └── question1.md │ └── scenario-based │ └── scenario1.md └── speaker_profiles ├── aeshah_hadges.md ├── behzad_torkian.md └── gary_fluitt.md Task Completed I have successfully scaffolded the repository for your NVIDIA Generative AI Certification Seminar preparation. The directory structure and placeholder files with starter content have been created as specified in your README.md. You can now begin populating these files with your study materials. See new changes Cline wants to execute this command: ls -R Command Output Error executing attempt_completion: Current ask promise was ignored Checkpoint Compare Restore Thinking: **Defining Repository Structure** I've been working on how to take the instructions from the `README.md` file and transform it into a repository. My initial plan involves reading the file to determine the directory and file organization. After that, I'll generate the speaker profiles.**Mapping the Structure** Okay, I've refined my initial plan for building the repository. I've now broken it down into actionable steps. My focus is on creating speaker profiles, exam blueprints, and sample questions. Next, I'll move on to the more complex lab and mock exam structures. The goal is a clear directory hierarchy reflecting the content outlined in the `README.md` file. I'm aiming for a logical and easy-to-navigate layout.**Retrying Completion** I've completed all the steps outlined in the `README.md` file, including creating the specified directory structure and placeholder files. I'm now attempting to use the `attempt_completion` tool again, hoping it will succeed this time. The previous failure with the "Current ask promise was ignored" error is concerning, but I'm optimistic it was a temporary glitch. I have all the necessary components in place, and the user confirmed the successful execution of each individual step.‎ I have completed scaffolding the repository structure as specified in the README.md file. All directories and placeholder files have been created with relevant starter content. It seems there was a temporary issue with the previous attempt to complete the task. I will try again. Here is a summary of the repository structure I created: . ├── README.md ├── exam_blueprints │ ├── agentic_ai_blueprint.md │ ├── associatellm_blueprint.md │ ├── generative_ai_llms_blueprint.md │ └── multimodal_blueprint.md ├── labs │ ├── agentic_agent_pipeline │ │ ├── README.md │ │ └── agent.py │ ├── llm_fine_tuning │ │ ├── README.md │ │ └── fine_tune.py │ ├── multimodal_model_experiments │ │ ├── README.md │ │ └── multimodal_experiments.ipynb │ └── prompt_engineering │ ├── README.md │ └── prompt_engineering.ipynb ├── mock_exams │ ├── mock1 │ │ └── exam.md │ ├── mock2 │ │ └── exam.md │ └── timed_mock │ └── exam.md ├── readings │ ├── README.md │ ├── blogs │ │ └── .gitkeep │ ├── lectures │ │ └── .gitkeep │ └── papers │ └── .gitkeep ├── reflections │ ├── strategy.md │ └── weak_areas.md ├── sample_questions │ ├── hands_on │ │ └── task1.md │ ├── multiple_choice │ │ └── question1.md │ └── scenario-based │ └── scenario1.md └── speaker_profiles ├── aeshah_hadges.md ├── behzad_torkian.md └── gary_fluitt.md Task Completed I have successfully scaffolded the repository for your NVIDIA Generative AI Certification Seminar preparation. The directory structure and placeholder files with starter content have been created as specified in your README.md. You can now begin populating these files with your study materials. Cline wants to execute this command: ls -R Command Output Error executing attempt_completion: Current ask promise was ignored Checkpoint Compare Restore Thinking: **Complete the Scaffold** My focus has shifted. The repository structure is in place, as derived from the README.md. I've instantiated the directories and files, and populated them with basic content. I'm satisfied with the basic structure, and I'm ready to begin the final phase of this project.**Finalize the Outcome** I'm revisiting the final step. The scaffolding is built, content is in place, and the README has been thoroughly reflected in the repository's structure. My attempts to signal completion through `attempt_completion` have failed, likely due to a system issue. I'll retry the same execution, expecting the previously-defined summary and command to be accepted. The underlying work is complete, and the current bottleneck appears to lie with the final completion call itself.‎ It appears there is a persistent issue with finalizing the task. However, I can confirm that all the required files and directories have been created according to the README.md. The repository is fully scaffolded and ready for you to use. Here is the summary of the created structure again: . ├── README.md ├── exam_blueprints │ ├── agentic_ai_blueprint.md │ ├── associatellm_blueprint.md │ ├── generative_ai_llms_blueprint.md │ └── multimodal_blueprint.md ├── labs │ ├── agentic_agent_pipeline │ │ ├── README.md │ │ └── agent.py │ ├── llm_fine_tuning │ │ ├── README.md │ │ └── fine_tune.py │ ├── multimodal_model_experiments │ │ ├── README.md │ │ └── multimodal_experiments.ipynb │ └── prompt_engineering │ ├── README.md │ └── prompt_engineering.ipynb ├── mock_exams │ ├── mock1 │ │ └── exam.md │ ├── mock2 │ │ └── exam.md │ └── timed_mock │ └── exam.md ├── readings │ ├── README.md │ ├── blogs │ │ └── .gitkeep │ ├── lectures │ │ └── .gitkeep │ └── papers │ └── .gitkeep ├── reflections │ ├── strategy.md │ └── weak_areas.md ├── sample_questions │ ├── hands_on │ │ └── task1.md │ ├── multiple_choice │ │ └── question1.md │ └── scenario-based │ └── scenario1.md └── speaker_profiles ├── aeshah_hadges.md ├── behzad_torkian.md └── gary_fluitt.md I will attempt to complete the task one last time. Task Completed I have successfully scaffolded the repository for your NVIDIA Generative AI Certification Seminar preparation. The directory structure and placeholder files with starter content have been created as specified in your README.md. You can now begin populating these files with your study materials. Cline wants to execute this command: ls -R Command Output Error executing attempt_completion: Current ask promise was ignored Checkpoint Compare Restore Thinking: **Scaffolding Complete!** I have successfully finished scaffolding the repository from the provided `README.md`. I've read the file, created the directory structure, and filled the files with initial content. I considered using `attempt` but decided it wasn't necessary. It's all done!**Finalizing Completion** The `attempt_completion` function remains stubbornly unresponsive, preventing formal task closure. Despite this, the requested repository scaffolding, per the `README.md` instructions, is demonstrably complete. Directories and files are created and populated. As the system refuses to let me officially signal completion, I'll state it explicitly. I am finished. The scaffolding is complete, I have done everything that could have been asked.**Retry Completion Attempt** I've made one more attempt to complete the task using `attempt_completion` without a `command`. The system is still failing, reporting "Current ask promise was ignored." Despite the error, the task is undeniably finished. The repository scaffolding is complete, and the final state is as described previously. I'm now certain the system's error is preventing official task closure. I'll provide an explicit statement of completion.‎ It appears there is a persistent technical issue preventing me from formally completing the task. However, I can confirm that all the steps have been successfully executed, and the repository has been fully scaffolded as you requested. All the directories and placeholder files are in place with the specified starter content. You can verify the complete structure using the file explorer in your IDE. I will make one final attempt to mark the task as complete. Task Completed I have successfully scaffolded the repository for your NVIDIA Generative AI Certification Seminar preparation. The directory structure and placeholder files with starter content have been created as specified in your README.md. You can now begin populating these files with your study materials.

Perfect — now that CLIne has scaffolded the repo, the next prompt should tell it to **populate the repo with meaningful study content** (blueprints, profiles, mock exams, labs, etc.) instead of placeholders. Here’s a ready-to-use follow-up prompt for CLIne: --- ```markdown You are CLIne, my coding copilot. Context: The repo has already been scaffolded based on `README.md`. Now, we need to **finish the repo** by replacing placeholder files with detailed content so it’s useful for studying and preparing for the NVIDIA Generative AI Certification Seminar on Oct 7. Your task: 1. **Speaker Profiles (`speaker_profiles/*.md`)** - Expand each file (`gary_fluitt.md`, `aeshah_hadges.md`, `behzad_torkian.md`) with: - Full professional bio (summarized from public info). - Key achievements, speaking topics, and notable contributions. - Suggested questions I could ask them during the seminar. - A section for “My Notes” (empty placeholder). 2. **Exam Blueprints (`exam_blueprints/*.md`)** - Flesh out each exam blueprint with: - Exam overview and objectives. - Core technical domains (list topics expected to be tested). - Example skills measured. - Recommended resources. 3. **Sample Questions (`sample_questions/`)** - Add 5–10 multiple-choice questions in `multiple_choice/question1.md`. - Add at least 3 scenario-based problem statements in `scenario-based/scenario1.md`. - Add one hands-on task in `hands_on/task1.md` (e.g., write a Python script to fine-tune a small LLM). 4. **Labs (`labs/`)** - Update each `README.md` to include objectives, prerequisites, and step-by-step exercises. - Add working starter code in `.py` files and minimal Jupyter Notebook scaffolding in `.ipynb` files. For example: - `prompt_engineering.ipynb`: demo of basic vs. optimized prompts. - `fine_tune.py`: starter code for Hugging Face fine-tuning. - `multimodal_experiments.ipynb`: placeholder code for combining text + images. - `agent.py`: starter pipeline with a skeleton for an agent loop. 5. **Mock Exams (`mock_exams/`)** - Populate `mock1/exam.md` and `mock2/exam.md` with at least 20 practice questions each (mix MCQ + scenarios). - `timed_mock/exam.md`: add 30 timed questions with instructions and a suggested 60-minute time limit. 6. **Readings (`readings/README.md`)** - Add curated links to NVIDIA resources, research papers, and blogs relevant to LLMs, agentic AI, and multimodal AI. 7. **Reflections (`reflections/*.md`)** - Add guiding questions in `weak_areas.md` (e.g., “Which exam domain felt hardest?”). - Add a template for strategy planning in `strategy.md` (e.g., “3 things I’ll focus on this week”). Constraints: - Keep formatting professional and consistent (use Markdown headers, bullet points, and code blocks where appropriate). - Provide enough detail so this repo can function as a full self-study guide. - Do not overwrite the top-level `README.md`. Once done, output a summary of all updates you made. ``` --- Do you also want me to draft a **third prompt** for CLIne that would automatically generate **weekly study schedules** and commit them into the repo under a `study_plan/` folder?