๐ ๐๐๐ถ๐น๐ ๐ ๐ช๐ฒ๐ฏ ๐ฃ๐ฎ๐ด๐ฒ ๐ฆ๐๐บ๐บ๐ฎ๐ฟ๐ถ๐๐ฒ๐ฟ ๐ช๐ถ๐๐ต ๐๐ I was onboarding a new Python library. The docs were scattered across 12 different HTML pages. I spent three hours clicking back and forth, copying snippets, and trying to piece together how the authentication flow worked. I thought: "There has to be a better way. Why can't I just dump all these pages into an AI and get a clean summary?" So I tried exactly that. And it worked. Sort of.
My first "solution" was manual. I opened each doc page, selected all text, pasted it into a single markdown file, and then fed that into ChatGPT. It worked for one page, but after three pages I wanted to scream. I decided to automate. My plan was simple:
- Fetch the HTML of each doc page
- Extract the main content
- Clean the text and split it into chunks
- Send each chunk with a summarization prompt
- Concatenate the summaries into one cohesive document
I wrote a Python script using requests, BeautifulSoup, and openai. When I ran this on two doc pages, I got back neat little summaries. But when I fed it five more pages, the problems piled up:
- Cost
- Context loss across chunks
- Hallucinated details
- Noise from bad HTML extraction
What I learned:
- Keep a human in the loop
- Chunk with overlap
- Consider using a cheaper model There are existing services that do exactly this. You can use this technique when:
- You're exploring a massive codebase
- You're trying to figure out if a library does something
- You want to generate a brief summary Avoid it when:
- You need precision
- The content is highly interconnected
- You're on a tight budget Source: https://dev.to/__c1b9e06dc90a7e0a676b/i-built-a-web-page-summarizer-with-ai-and-why-you-might-not-want-to-26fi