Build Anything With ChatGPT API, Here’s How
Based on David Ondrej's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.
Create a secret API key on platform.openai.com, then store it in an environment variable immediately because it can’t be retrieved later.
Briefing
Building an AI app with the ChatGPT API can be done quickly once an API key and a basic prompt-and-PDF pipeline are in place. The workflow starts with creating a secret API key on platform.openai.com (after logging in with the same account used for ChatGPT and verifying a phone number if prompted). Because the key can’t be viewed again, the practical move is to copy it immediately and store it in an environment variable (for example, in an .env file) rather than hard-coding it into source code.
From there, the project shifts to a “book summarizer” concept inspired by Blinkist: take a long book, ask the model to extract passages relevant to a user-chosen topic, and return a structured summary. The build uses GPT-4 (specifically referenced as having a 128,000 token window) with tunable generation settings: temperature is set lower (0.3) to make outputs more deterministic, and max tokens is capped (around 500) to control response length. The app also parameterizes the topic so the same code can summarize different themes—like “money,” “happiness,” or “fitness”—without rewriting the logic.
A key implementation detail is prompt engineering to avoid refusals tied to copyrighted text. The approach uses a separate prompts.py file containing a system message plus a helper function that generates the main prompt. The prompt is designed to “convince” the model that the text being summarized is the user’s own content, using a crafted narrative framing. The example book is the “Almanak of Naval Ravikant” (242 pages), with a PDF sourced from a free link. Because PDFs aren’t plain text, the pipeline uses PDF processing (via PyPDF2) to extract page content.
The transcript also highlights a messy but realistic preprocessing step: the code removes the first 23 pages and the last 30 pages because the model reportedly reacts poorly to certain front/back matter. The script loops through the remaining pages, concatenates them into a single “book” string, and then feeds that text into a chat completion request. After a test run, the output is checked for refusal behavior and the cost is monitored through the API usage/billing dashboard, where daily spend is tracked.
Overall, the takeaway is less about one specific summarizer and more about the repeatable pattern: authenticate with an API key, store it securely, install required libraries, load a PDF into text, craft system and user prompts carefully, and call the ChatGPT API using the documented chat completions endpoint. The transcript frames this as an opportunity for rapid experimentation—especially for niche applications that large AI companies may ignore—because the API removes the need to build and run language models from scratch.
Cornell Notes
The project demonstrates a practical way to build a custom book summarizer using the ChatGPT API. It begins with creating a secret API key on platform.openai.com, storing it in an environment variable, and installing the needed Python libraries (including PDF parsing). The core logic loads a PDF, strips problematic pages, concatenates the remaining text, and sends it to a GPT-4 chat completion request with a system message and a topic-driven prompt. Prompt engineering is used to reduce the chance of refusals when summarizing copyrighted material. The result is a repeatable template for turning long-form documents into topic-focused summaries while tracking API usage and cost.
What are the minimum setup steps needed before any summarization code can run?
How does the summarizer decide what to summarize when the user changes topics?
Why does the transcript emphasize prompt engineering for book summaries?
What preprocessing is applied to the PDF before sending it to the API?
How are GPT generation parameters tuned in this example?
How is cost and usage monitored during development?
Review Questions
- What steps ensure the API key is handled securely, and why does the transcript discourage hard-coding it in source code?
- How does the code structure prompts (system message vs. generated user prompt), and how does that relate to avoiding refusals?
- Why does the PDF summarization pipeline remove the first 23 pages and last 30 pages, and what would happen if it didn’t?
Key Points
- 1
Create a secret API key on platform.openai.com, then store it in an environment variable immediately because it can’t be retrieved later.
- 2
Use a dedicated prompts.py file with a system message and a prompt generator so topic changes don’t require rewriting the app.
- 3
Install and use PyPDF2 to extract text from PDFs; long documents require preprocessing rather than treating PDFs like plain text.
- 4
Trim problematic PDF sections (the example removes the first 23 pages and last 30 pages) before sending content to the model to reduce refusal or poor outputs.
- 5
Tune generation settings for consistency: set temperature to 0.3 and cap max tokens (around 500) for controlled summaries.
- 6
Feed the model a concatenated “book” string and call the chat completions endpoint, then extract only the returned message content.
- 7
Track spend in the OpenAI dashboard (usage/billing) to keep iteration costs predictable during development.