Set up your GPT assistant in a real workspace
If you’ve ever shouted at your screen because ChatGPT misunderstood something obvious like “summarize the first part only,” you’re not alone. So let’s start with the only part that consistently works: setting up a reliable workspace that stays scoped to one job. Not ten things open in the sidebar. Not six modes switching every time you breathe.
Here’s how I run it now:
– I’ve got one project folder in Notion, named with the actual question I’m working on, like: “Quick Review Of LLM Claims In 2022 Papers.” Inside that folder: the target paper as a PDF, an empty text page where I stick bullets, raw dumps, and brainstorms, and a status note (“stuck because hallucination in abstract again lol”).
– Then I open up ChatGPT and immediately turn on the PDF Tools plugin or Heygen (depending which week it is — some plugins are given and then taken away with no warning ¯\_(ツ)_/¯).
– I pin that one chat. Only that one. No side experiments. And I always rename it to match the Notion title. GPT sometimes forgets recent context, but I don’t.
If you don’t do that, you type a question and it half answers about an entirely different PDF you uploaded the day before. Or worse, it finds data from your last unrelated chat and assumes that’s still relevant. At one point it insisted all three papers I uploaded had “section 3 labeled Comparison Results” which made zero sense (and kept it up across 4 follow-ups). Just pin and label everything.
Upload versus copy paste here is what fails
Personally I hate the file uploader in ChatGPT when it comes to academic papers. If the scanner embedded the whole PDF as one big image (which happens with public research PDFs more often than you’d think), GPT sees nothing. Uploads silently fail. It responds as if there’s no content — doesn’t tell you why.
You’ll get this vague response:
> “Unfortunately I cannot extract meaningful information from this file.”
When that happens, opening the file in your browser usually shows you one giant image. Copy-pasting the text manually (if possible) helps. Ironically, split-pasting (like 2000 tokens at a time) gives you better consistency than trusting GPT to extract anything from PDF structure.
Also, footnotes are evil. If you paste an excerpt and include the references at the bottom (even if it’s just numbers like [1], [2], [3]), it starts summarizing based on citation patterns instead of sentence meaning. I once had it say:
> “The authors argue that…[4][5] contradicts this.”
No, they didn’t. That was just pasted inline. Lesson: clean the input yourself. Don’t feed GPT an untouched wall of footnotes and hope it’s smart enough to ignore them 🙂
Prompt for isolated sections or parts only
Here’s a common mistake I keep making even though I know better — saying “summarize this paper” and expecting a clean top-to-bottom summary that doesn’t invent things or conflate intro with results.
What works better is:
– “Read only the first three paragraphs and tell me the stated goals of this paper.”
– “Summarize only the method section. I will paste more after.”
– “Ignore anything outside section 2.3. Assume earlier parts do not exist.”
This feels dumb to have to say, but GPT absolutely *will* pull in ideas from context it wasn’t supposed to use unless you tell it not to. Even if you *just* pasted section 2.3, it’s still trained to guess based on full-text memory. Tell it explicitly what scope to look at.
Oh and any mention of tables or figures in the paper? GPT hallucinates them unless you give it the captions manually. Otherwise you’ll get nonsense like:
> “Figure 4 shows a significant drop in error margin.”
…when there was no Figure 4. There were only two diagrams. I once asked for a table comparison summary and it gave me a totally fictional set of values (which didn’t even match the source units). So again — paste the numbers, don’t rely on it to guess.
Confirm definition and goal before summarizing
A huge mistake I kept repeating early on was skimming over the intro and jumping straight into a “summarize this” prompt. But GPT plays a guessing game if you don’t give it framing. I’ve seen it label a population study as an algorithmic breakthrough — just because the results looked numerical.
Here’s what I now do differently (and yes, it sounds basic until you see how badly things break otherwise):
First prompt:
– “What type of study is this? Don’t summarize, just tell me what kind it is: literature review, experimental trial, meta-analysis, position paper, etc.”
Second prompt:
– “What was the authors’ original stated objective?”
Only *then* do I let it summarize anything. Jumping into summarization without understanding the paper’s category leads to easy hallucinations:
> “The authors compared three algorithms” (they didn’t — they reviewed prior work)
> “They deployed a new model on testing data” (nope — it was a policy essay)
So yeah, trust but verify. Especially if you’re skimming PDFs fast for a deadline.
Track which info came from which paper
This one drove me nuts. I wanted to compare insights from three papers side by side, so I uploaded them all in one chat. GPT gladly summarized… but *blended all the details together*. Then referred to “the paper” as if there was only one.
So now I make GPT call each paper Paper A, B, and C at the start. Like this:
> “Call the first paper ‘Paper A’ and only refer to it by that shorthand. Wait for me to upload the second.”
Then when I say “Summarize Paper A versus Paper C,” it knows what I mean. Without that prep, it starts merging studies and attributing Source B’s results to Source A. At one point it confidently claimed the wrong sample size and methodology — just because I uploaded papers in the same chat window in the wrong order ¯\_(ツ)_/¯
Also helpful: after pasting something, say things like “This belongs to Paper B” so it logs context correctly. Otherwise you’ll get things like:
> “Paper A found a decrease in latency” (nope — Paper C did that.)
Force GPT to avoid conclusions without proof
I get that GPT is trained to sound confident. But research paper summarization is the worst place for overly confident phrasing. I had it tell me:
> “Clearly, the results demonstrate improvement.”
No, *clearly* nothing. That phrase wasn’t even in the paper. Researchers hedge nonstop — “suggest,” “may indicate,” “not statistically significant.” If GPT starts giving you strong language, call it out:
Try this:
– “Avoid conclusive language. Do not say ‘clearly’, ‘definitely’, or ‘demonstrated’ unless the authors use those exact words.”
– “Do not paraphrase statistical significance. If not reported, say so.”
When forced to match tone, you suddenly see how often GPT guesses or oversells. It’s fine for blog content, but if you’re summarizing findings for a grant or lit review, that tone will instantly get flagged by someone who actually read the source. 😛
Extract citations or sources cleanly
Sometimes all I want is to know which other studies a paper references. Citation mining is a pain when PDFs are scanned — GPT kind of helps, but not on autopilot.
Paste in a chunk and ask:
– “Ignore main text. Extract only references at the bottom. Format as [Author Year] style list.”
If the paper uses weird inline references like superscript numbers (like¹, ², etc.), GPT sometimes just skips them. In that case, say:
– “Return a numerical list of citations matching the source order, even if numbers are embedded.”
If you ask for Source X and it says “not found,” it may be hiding in a split paragraph you didn’t paste yet. I’ve had to manually stitch together citations across page breaks before it would admit they existed. Awful experience.
Also be ready for markdown weirdness. If you ask GPT to return a list in bulleted format, it sometimes assumes you want formal APA citation formatting. You’ll get this:
> “Smith, J., & Allen, R. (2020)…”
When all you wanted was:
– [Smith 2020] Comparative frameworks
Clarity matters in the ask.