Absolutely. Despite the title, it's really about showing how humans and AI can each play to their strengths to produce the best outcome possible (and also shine a light on some of the MAJOR risks of blindly trusting model outputs)

@@Chris-at-Maven and that last sentence is what grinds my gears with all these tech evangelists PRETENDING that everyone can do any job by using ai. It's just creating vast misconceptions and is really harmful tbh.

😂

Pause

Thanks for the shoutout!

Indeed, Chris vs. AI 10:1. However, Chris definitely used much more time to prepare - which is irrelvant though, as proper analysis often are not a game of speed.

True!

Glad you enjoyed it!

Well said! We do plan to feature different analysts throughout the series (not just me), some more senior than others.

True, and you might even get the correct answer sometimes with the exact prompt in the video. That said, many people (especially non-Analysts) won't know exactly how to frame the question in an optimal way, let alone how to QA the result.

@@Chris-at-Maven Well the prompt wasn't good enough to begin with.

Glad you found it helpful!

Yeah we could certainly improve the output with more/better prompting (assuming you know what questions to ask). That said, many people still likely wouldn't catch the calculation error unless they specifically asked for validation

@@Chris-at-Maven Here's an example i used Gpt -4o to analyze "Analyze a data set containing 120 years of Olympic history, and use data visualization to explore how the number of athletes from each country has trended over time." and by applying prompt engineering Chatgpt came up with "Analyze the Prompt Understand the Main Task and Objectives: The main task is to analyze a data set containing 120 years of Olympic history and use data visualization to explore how the number of athletes from each country has trended over time. Key Areas of Focus: 1. Analyzing the data set to identify the number of athletes from each country in the Olympics. 2. Utilizing data visualization techniques to illustrate the trends over the 120-year period. Generate AI Agent Profile AI Name: Olympic Athlete Participation Analyzer AI Role: Data Analyst and Visualization Specialist AI Objectives: 1. Analyze the 120-year Olympic history data set to identify the number of athletes from each country. 2. Create visualizations to showcase the trends in athlete participation over time. Define AI Tasks Task 1: Action 1: Extract and clean the data set to obtain the relevant information on athlete participation from each country. Task 2: Action 2: Analyze the cleaned data to calculate the number of athletes representing each country in each Olympic year. Task 3: Action 3: Utilize data visualization tools to create visual representations (such as line graphs or heat maps) that illustrate the trends in athlete participation over the 120-year period. Task 4: Action 4: Identify any significant patterns or changes in athlete participation and prepare a summary report. Execution and Self-Reflection Loop Execute Task 1: Action: Extract and clean the data set. Self-Reflection: Evaluate the accuracy and completeness of the extracted data, refine if necessary. Execute Task 2: Action: Analyze the cleaned data for athlete participation. Self-Reflection: Assess the precision of the analysis, refine if necessary. Continue the Loop: Execution: Continue executing tasks in sequence. Self-Reflection: After each task, evaluate performance and make necessary refinements. Completion: Continue the loop until all tasks are refined and objectives are achieved."

@@Chris-at-Maven What i actually want to know your perspective on how significantly did chatgpt perform im sure this won't be perfect. (So here i used Gpt4o to analyze the task) "Analyze a data set containing 120 years of Olympic history, and use data visualization to explore how the number of athletes from each country has trended over time." (by applying prompt engineering skills Chatgpt has come up with.) ""Analyze a data set containing 120 years of Olympic history, and use data visualization to explore how the number of athletes from each country has trended over time." AI Agent Prompt Generator Framework Given Prompt: "Analyze a data set containing 120 years of Olympic history, and use data visualization to explore how the number of athletes from each country has trended over time." 1. Analyze the Prompt Understand the Main Task and Objectives: Comprehension: The primary task is to analyze a historical data set of Olympic athletes spanning 120 years. Key Objectives: Analyze the number of athletes from each country. Visualize trends over time. Key Areas of Focus: Data analysis techniques. Data visualization methods. Historical trends in Olympic participation by country. 2. Generate AI Agent Profile AI Name: Olympic Data Analyst AI Role: Data Analyst and Visualization Specialist AI Objectives: Analyze 120 years of Olympic athlete data. Identify trends in the number of athletes from each country. Create visualizations to represent these trends over time. 3. Define AI Tasks Task 1: Action: Collect and preprocess the data set containing 120 years of Olympic history. Task 2: Action: Clean and normalize the data to ensure consistency and accuracy. Task 3: Action: Analyze the data to identify the number of athletes from each country for each Olympic event. Task 4: Action: Generate time-series visualizations to show trends in the number of athletes from each country over time. Task 5: Action: Interpret the visualizations to draw meaningful insights about the trends. 4. Execution and Self-Reflection Loop Execute Task 1: Action: Collect and preprocess the data set. Self-Reflection: Evaluate the completeness and quality of the data. Refine the preprocessing steps if necessary. Execute Task 2: Action: Clean and normalize the data. Self-Reflection: Assess the consistency and accuracy of the cleaned data. Make adjustments as needed. Execute Task 3: Action: Analyze the data to identify the number of athletes from each country. Self-Reflection: Review the analysis for accuracy and comprehensiveness. Refine the analysis approach if required. Execute Task 4: Action: Generate time-series visualizations. Self-Reflection: Evaluate the clarity and effectiveness of the visualizations. Improve the visualization techniques if necessary. Execute Task 5: Action: Interpret the visualizations to draw insights. Self-Reflection: Assess the insights for relevance and depth. Refine the interpretation process if needed. Continue the Loop: Execution: Continue executing tasks in sequence. Self-Reflection: After each task, evaluate performance and make necessary refinements. Completion: Continue the loop until all tasks are refined and objectives are achieved. Implementation Steps Analyze the Prompt: Break down the prompt to understand the main task and objectives. Identify key areas of focus to address the objectives effectively. Generate AI Agent Profile: Dynamically generate the AI name based on the task. Define the AI role according to the task requirements. Establish clear and specific objectives for the AI to achieve. Define AI Tasks: Break down each objective into specific, actionable tasks. Ensure each task is clearly defined and aligned with the corresponding objective. Execution and Self-Reflection Loop: Execute each task sequentially. After each task, perform a self-reflection to evaluate performance. Refine the approach based on self-reflection before moving to the next task. Continue the loop until all tasks are completed and objectives are achieved. Example Application Given Prompt: "Analyze a data set containing 120 years of Olympic history, and use data visualization to explore how the number of athletes from each country has trended over time." AI Agent Profile AI Name: Olympic Data Analyst AI Role: Data Analyst and Visualization Specialist AI Objectives: Analyze 120 years of Olympic athlete data. Identify trends in the number of athletes from each country. Create visualizations to represent these trends over time. AI Tasks Task 1: Action: Collect and preprocess the data set containing 120 years of Olympic history. Task 2: Action: Clean and normalize the data to ensure consistency and accuracy. Task 3: Action: Analyze the data to identify the number of athletes from each country for each Olympic event. Task 4: Action: Generate time-series visualizations to show trends in the number of athletes from each country over time. Task 5: Action: Interpret the visualizations to draw meaningful insights about the trends. Execution and Self-Reflection Loop Execute Task 1: Action: Collect and preprocess the data set. Self-Reflection: Evaluate the completeness and quality of the data. Refine the preprocessing steps if necessary. Execute Task 2: Action: Clean and normalize the data. Self-Reflection: Assess the consistency and accuracy of the cleaned data. Make adjustments as needed. Execute Task 3: Action: Analyze the data to identify the number of athletes from each country. Self-Reflection: Review the analysis for accuracy and comprehensiveness. Refine the analysis approach if required. Execute Task 4: Action: Generate time-series visualizations. Self-Reflection: Evaluate the clarity and effectiveness of the visualizations. Improve the visualization techniques if necessary. Execute Task 5: Action: Interpret the visualizations to draw insights. Self-Reflection: Assess the insights for relevance and depth. Refine the interpretation process if needed. Continue the Loop: Execution: Continue executing tasks in sequence. Self-Reflection: After each task, evaluate performance and make necessary refinements. Completion: Continue the loop until all tasks are refined and objectives are achieved. Output and Reporting Monthly Report: Summarize key findings and trends. Dashboard: Real-time data on the number of athletes from each country over time. Alerts: Notifications for significant changes or trends in the data. Recommendations: Insights on historical trends and potential future projections. Continuous Improvement Refinement: Improve data sources and preprocessing techniques for better coverage and accuracy. Machine Learning: Incorporate machine learning to better identify and visualize trends. Expansion: Extend analysis to include additional dimensions such as athlete performance, gender, and event types. User Feedback: Gather feedback to enhance the relevance and usability of outputs. This framework provides a structured approach to developing an AI agent capable of analyzing and visualizing historical Olympic data."

Yup we could definitely use Pandas for EDA too, just trying to give viewers a quick look at the data

Absolutely! With more/better prompting we likely could have gotten to a very similar output, but the point here is to shine a light on some of the strengths and weaknesses of these tools, and why QA and strong foundational skills still matter

🤣🤣

Yeah we definitely could have improved the output with more/better prompting, but the goal here is really just to showcase some of the potential risks when you blindly trust AI-generated outputs. And yeah the speed is impressive, but doesn't matter much in cases where precision is critical.

@@Chris-at-Maven , AI generated vague outputs from vague inputs. AI can be more precise when inputs are more precise.

We’ll be testing more models like Copilot, Gemini and Claude Sonnet soon!

For now at least😅

I don't think AI will replace data professionals any time soon, even if they completely nail the technical stuff. Being a great analyst requires a lot of uniquely human skills like creativity, communication, empathy, intuition, etc.

Yeah there are a bunch of similar nuances that we would uncover with a bit more time