Setting up the initial prompt workflow environment
First off, you’ll need at least two stable things: the PDF you want to rip into content, and a tool that allows uploading and prompting it intelligently. Obviously ChatGPT Plus with GPT-4 can do this, but so can tools like Claude and Poe — I’ve used all three interchangeably depending on which tab I forgot I already had open 😛
To keep things consistent here, I’ll walk through what I do in ChatGPT Plus. It’s still the one that cooperates most of the time — at least when it isn’t suddenly acting like a Labrador with no memory.
What I usually do:
1. Open a new GPT-4 chat window
2. Upload the PDF via the paperclip icon
3. Immediately tell the model what I want: something like “I need to turn this PDF into a human-style blog draft. Don’t copy anything. Use it as a reference.”
Now here’s the kicker: If you don’t immediately tell the model the job, it sometimes just treats the PDF like a doc viewer. I’ve had it give me garbage like “This appears to be a presentation” (no kidding…) — so cue the Ctrl+W tab rage.
If your PDF is long or has charts/images, be prepared. Sometimes it chokes. I’ve had better luck breaking up PDFs beforehand.
Also, if the file is scanned or the text is flattened (like an exported image), you’re going to want OCR — I either run it through Adobe Acrobat (the hellspawned desktop version), or upload it to Google Drive and open with Google Docs to convert rough text into real text.
Breaking the PDF into contextual chunks
Okay so here’s the thing: if you just upload a massive 20-page PDF and say “make a blog post,” be prepared for chaos. GPT may pull from completely irrelevant parts, repeat itself four times across sections, or hallucinate subheadings that don’t exist anywhere in the file ¯\_(ツ)_/¯
I usually go manual. I split the document into functional segments:
– Introduction or executive summary stuff
– Sections that have clear headers or layout breaks
– Tables or enumerated content that could become bullets
– Case studies or example-rich pages to save for a story section
I copy-paste EACH part (yes, manually) and drop it into the same GPT thread, but label them. I say something like:
“This is Section 2 — product specs in dense paragraph form. Focus on the general sentiment, not phrasing.”
Or:
“Section 4 — example use case we might want to turn into a story or quotation-style paragraph in the blog.”
It matters more than I want it to. I’ve tried automating this step with Zapier + Dropbox OCR APIs but 80% of the time it just failed silently or brought back invisible characters. So yeah, copy-paste for now.
Crafting the base prompt to guide the draft
This part took me a very embarrassing number of tries to get right. I kept thinking “shorter prompts are better” — nope. For blog draft workflows built from PDFs, the key is giving Positional Authority. Basically, treat GPT like a personal writer who JUST read 20 pages and now has to summarize it but sound fun, not like they hate their job.
Here’s a prompt I saved that works fairly well (use it after you’ve uploaded and explained each section):
—
“You now have all the background info from the original document. Write a casual, helpful, and human-sounding blog post that covers what this content teaches someone, without copying language or structure. Don’t summarize. Don’t just list things. Act like you’re talking to someone trying to use this info in real life.”
—
I tack on my style tone if needed. Something like:
“Write as someone who is constantly over-engineering their workflow because nothing ever works the same twice. Use a human tone, no boring structures.”
Honestly, just ordering GPT not to write a summary took away at least three weird output patterns. For whatever reason, if you say “summary,” it defaults back to robotic “First, Second, Third” writing like it’s on autopilot.
Fixing structure problems in the first draft
The first response you get back… is usually so chaotic it feels like someone tried to write a blog post using only search autocomplete suggestions. You’ll have paragraphs that wander. Transitions that forgot what paragraph they’re in. Overlapping thoughts.
Don’t accept the first draft unless it’s miraculously solid (which has happened only twice, and yes, I screenshotted both). I usually step in right after the first big chunk and say:
“Hold up. This paragraph here doubled up with the one above. Can you merge or pick one?”
Then further down:
“This transition here makes no sense. Try making it conversational instead of repeating the previous sentence.”
Or even:
“You said ‘automate your daily tasks’ three different ways. Pick just one and make it sound like an actual human.”
Honestly, this part is just live editing. Ask it to regenerate paragraphs individually. I never blow away the whole answer — that loses context.
Translating dense tables into readable bullets
PDFs with tables are always a mess. Even when GPT reads them correctly, it tends to regurgitate the cells like:
“Feature One: Fast. Feature Two: Faster. Feature Three: Also Fast.” 🙃
So what you really want is to break each row into a small explainable bullet and ask GPT:
“Turn this table into a readable list of features with context and opinion included.”
And paste the raw table data like:
– Name: UltraTurbo Blender 3000
– Power: 1200 Watts
– Notes: Medium noise, 3 speeds only
That gets you:
“One standout is the UltraTurbo Blender 3000, which runs at 1200 Watts — enough to crush ice in seconds — but you’re locked into 3 speed settings. Also, don’t expect it to whisper while running.”
Way better.
Sometimes I do post-processing into a React-style bullet generator in another window when writing for clients. Wildly overengineered, but again… it worked once 😅
Getting the right structure of subheadings
This one burned me a bunch of times. Left to its own devices, GPT will generate semantically nice but useless subheadings like “Understanding the Basics” or “Getting Started with PDF Content.” Those don’t solve reader problems.
So I always step in *before* generation and say:
“Give me 6 to 9 actionable subheadings based on the PDF. Each one should answer a different implied reader question.”
Good examples:
– Extracting usable wisdom from a dense PDF
– Translating B2B marketing slides into blog posts
– Fixing formatting hell from OCR-mangled content
Refine twice: First generate the list; then tweak them manually or ask GPT to regenerate more based on the voice you need.
If you already got a bloated 10-section structure, just say:
“Consolidate to 7 max. Drop anything that sounds like an intro or wrap-up.”
That avoids the typical bookend fluff.
Using system messages to preserve your tone
You can inject a system message (if using the GPT API or building with custom GPTs) to permanently set the tone. I have a custom setup that includes:
“You are a chaotic but capable blog writer juggling too many things. Always narrate real experiences and mechanical gotchas, and never sound like a corporate newsletter.”
This seriously helps keep outputs sounding human *without* having to re-prompt every five minutes.
System messages work way better than user instructions in keeping internal consistency, especially when converting large documents. I’ve tested this in API-powered workflows that go like: upload PDF → split via regex → dump into GPT chat threads with fixed system message → get readable outputs almost every time.
Exporting and final cleanup formatting
Last part: getting the usable copy out of GPT and into the blog CMS. Copy-paste is fine, but markdown never comes out clean.
I use a filter pass in VS Code (yeah I know, overkill) where I:
1. Strip weird break tags
2. Convert bullets that use hyphens into actual unordered list items
3. Fix any headings that came out of GPT as bolded multiline divs 😩
There’s also a Notion importer setup I kludged together with Make that sometimes works. Sometimes.
Anyway, once I clean it, I read the post out loud once. That always reveals the weirdly repeated bits (“Wait did I say ‘chaotic market trends’ twice in this sentence?”).
Then it gets queued. Or lost in my backlog of drafts-with-names-like-finalfinal2untilreadyactuallyfinal.