| 3 - Module 2 Core Prompting Techniques | |||
| 4 - Module 3 Production Prompting | |||
| 10 - M03A2 Schemas Nested Data.mp4 | 57 MB | ||
| 11 - M03A3 Validation Verification.mp4 | 125.3 MB | ||
| 12 - M03B Error Handling.mp4 | 171.8 MB | ||
| 13 - Introduction To Classes Video On Youtube.txt | 0 B | ||
| 13 - M03C Prompt Templates.mp4 | 166.8 MB | ||
| 14 - M03D Prompt Evaluation.mp4 | 87.6 MB | ||
| 5 - Module 4 Advanced Prompting | |||
| 15 - M04A Instructions Conversation Chaining.mp4 | 128.4 MB | ||
| 16 - M04B Roles Personas.mp4 | 210.5 MB | ||
| 17 - M04C Few Shot Advanced.mp4 | 95.1 MB | ||
| 18 - M04D Selfconsistency.mp4 | 81.1 MB | ||
| 19 - M04E Capstone 1 Research Assistant.mp4 | 217.6 MB | ||
| 6 - Module 5 Production Applications | |||
| 20 - M05A Token Counting Cost Tracking.mp4 | 75.1 MB | ||
| 21 - M05B Context Window Strategies.mp4 | 152.6 MB | ||
| 22 - M05C Response Caching.mp4 | 69.6 MB | ||
| 23 - M05D Capstone 2 Production Support Bot.mp4 | 164.5 MB | ||
| 7 - Module 6 Function Calling | |||
| 24 - M06A Function Calling Basics.mp4 | 189 MB | ||
| 25 - M06B Multiple Functions.mp4 | 164.1 MB | ||
| 26 - M06C Capstone 3 Multitool Agent.mp4 | 158.2 MB | ||
| 9 - M03A1 Getting Json From Ai.mp4 | 111.7 MB | ||
| 7 - M02A Zeroshot Oneshot Prompting.mp4 | 137.6 MB | ||
| 8 - M02B Few Shot Prompting.mp4 | 111.6 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| environment.yml | 1.5 KB | ||
| ~Get Your Files Here ! | |||
| 1 - Course Overview | |||
| 1 - Overview.mp4 | 105.4 MB | ||
| 2 - Module 1 Setup | |||
| 2 - Course Setup Guide.mp4 | 231.7 MB | ||
| 2 - Course Setup Guide.txt | 102.4 B | ||
| 3 - Daily Start Bookmark This.txt | 102.4 B | ||
| 3 - M01A Setup Test.mp4 | 46.9 MB | ||
| 4 - M01B Openai Setup.mp4 | 76.7 MB | ||
| 5 - M01C Responses Api.mp4 | 201.7 MB | ||
| 6 - M01D Helper Functions.mp4 | 86.4 MB | ||
| prompt-eng | |||
| M01_Setup | |||
| M01A_Setup_Test.ipynb | 7.7 KB | ||
| M01B_OpenAI_Setup.ipynb | 16 KB | ||
| M01C_Responses_API_Basics.ipynb | 13.2 KB | ||
| M01D_Helper_Function.ipynb | 11.7 KB | ||
| M01Z_Migration_From_Completions.ipynb | 10 KB | ||
| M02_Core_Prompting_Techniques | |||
| M02A_Zero_Shot_One_Shot_NB.ipynb | 18 KB | ||
| M02A_Zero_Shot_One_Shot_Solutions.ipynb | 7 KB | ||
| M02B_Few_Shot_Prompting.ipynb | 18.3 KB | ||
| M03_Production_Prompting | |||
| M03A_Structured_Outputs.ipynb | 36.5 KB | ||
| M03B_Error_Handling.ipynb | 26.8 KB | ||
| M03B_Solution_Error_Handling.ipynb | 9.2 KB | ||
| M03C_Prompt_Templates.ipynb | 25.5 KB | ||
| M03D_Prompt_Evaluation.ipynb | 19.4 KB | ||
| M04_Advanced_Prompting | |||
| M04A_Instructions_Chaining.ipynb | 18.5 KB | ||
| M04B_Roles_Personas.ipynb | 17 KB | ||
| M04C_Few_Shot_Advanced.ipynb | 16.9 KB | ||
| M04D_Self_Consistency.ipynb | 16.9 KB | ||
| M04E_Capstone_1.ipynb | 24.5 KB | ||
| M04E_Solutions.ipynb | 15.3 KB | ||
| M05_Production_Applications | |||
| M05A_Token_Counting_Cost_Tracking.ipynb | 10.2 KB | ||
| M05B_Context_Window_Strategies.ipynb | 18.8 KB | ||
| M05C_Response_Caching.ipynb | 8.6 KB | ||
| M05D_Capstone_2.ipynb | 18.5 KB | ||
| M05D_Solutions.ipynb | 14.7 KB | ||
| M06_Function_Calling | |||
| M06A_Function_Calling_Basics.ipynb | 24.9 KB | ||
| M06B_Multiple_Functions.ipynb | 24.6 KB | ||
| M06C_Capstone_3.ipynb | 28.9 KB | ||
| M06C_Solutions.ipynb | 18 KB |
Prompt Engineering with Python & OpenAI (ChatGPT) API
https://WebToolTip.com
Last updated 4/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.34 GB | Duration: 5h 51m
Build Production AI Apps With Python - Master Prompting, Function Calling, Error Handling With 25+ Hands-On Notebooks
What you'll learn
Connect Python to OpenAI’s ChatGPT API and generate structured outputs
Write Python functions that integrate multiple live APIs (NewsAPI, Google Trends, Federal Reserve data)
Parse and analyze JSON responses to extract meaningful insights
Automate news sentiment classification using AI-powered text analysis
Visualize sentiment and trend data with simple Python charts and dashboards
Combine multiple data sources into a unified scoring framework for company or industry analysis
Apply error handling and rate limiting to create reliable API workflows
Adapt the same pipeline patterns to competitor tracking, brand monitoring, and regulatory intelligence projects
Requirements
Basic Python familiarity: classes, functions, loops, and importing libraries
Ability to read simple JSON data and error messages
No prior Jupyter Notebook experience (setup and usage explained in the course)
No advanced math, finance, or machine learning background needed
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