If you've ever stared at a blank chat box wondering why your AI assistant keeps giving you the academic equivalent of a fortune cookie response, you're not alone. During our recent Scite Summer Boot Camp session on effective prompting techniques, we discovered that most researchers treat AI tools like they're Google with a PhD.
And that's exactly where things go sideways.
Prompting an AI assistant for research isn't about throwing keywords at it and hoping something sticks. It's more like conducting an interview with a brilliant but literal-minded colleague who needs extremely specific instructions. The quality of your research output correlates directly with the precision of your prompts. Generic in, generic out.
Chris Bendall, our VP of Product Strategy and a geochemist with a PhD who's spent years wrestling with research databases himself, kicked off the session with a reality check that likely hits home for many researchers. While pointing to a real-world prompt example about SGLT2 inhibitors, he emphasized that although most researchers know they should provide context and background, what really makes the difference is adding those specific limitations and constraints (e.g., explicitly stating "do not include or extrapolate if they are not available" or "do not include anything about SGLT1." It's these precise parameters that prevent the AI from wandering off into irrelevant territory, and their absence that causes most research prompts to fail."
The root of the problem lies in how we use these tools. Most of us still treat AI assistants like search engines: type in a couple of keywords, hit enter, and hope for relevance. But Large Language Models don’t think in keywords; they think in context. They’re conversation partners, not filing cabinets. When you feed them two words, you’re essentially asking them to write a dissertation based on a Post-it note.
Take a simple example: type “GLP-1 agonist” into a research AI, and it’ll try to be helpful by generating searches for diabetes treatment, clinical trials, and mechanisms of action—basically everything under the sun. But refine the prompt to: “Using papers published after 2018, give me an overview of GLP-1 agonists and areas of future research related to adverse effects,” and the output changes completely. Now the AI knows exactly what you want, when you want it from, and how to frame it.
The secret to making prompts actually work lies in understanding their architecture. Think of it like building a research query sandwich; every layer matters:
Context and Purpose forms your foundation. Start with why you're asking. "I'm preparing a background section for a review article" tells the AI you need comprehensive, citation-heavy content, not a quick summary for a grant deadline.
Specific Request is your meat and potatoes. Don't just ask about side effects. Specify "major cardiovascular adverse events" or "volume depletion events including hypotension, dehydration, and syncope." The AI can't prioritize what you don't specify.
Focus Area is the seasoning that highlights what matters most. Emphasize the aspects you want the AI to spotlight, such as safety, efficacy, mechanisms of action, or emerging trends. Without this layer, you risk getting a broad response that misses the key points you care about.
Parameters and Constraints act as your guardrails. Set your timeframes ("published since 2020"), study types ("focusing on empirical studies"), or geographic limits. Without these, you're asking the AI to summarize all of human knowledge on your topic.
Output Format is the garnish that makes everything usable. Want three bullet points? Say so. Need it in APA format? Specify that. Otherwise, you might get a wall of text when you needed a table, or vice versa.
Scite Assistant’s settings panel is one of its most underused features, and one of its most powerful. Hidden behind the small gear icon is a full range of control over how the AI responds that help users fine-tune precision, tone, and depth. Think of it like driving a car: you can stick to first gear and get where you’re going, or you can actually use the transmission and feel the full power of what’s under the hood.
The evidence source strategy alone elevates the quality of insights. Want the most up-to-date findings from 2025 that haven’t been cited yet? Use "abstracts only." Need rock-solid, evidence-backed claims for a regulatory submission? Switch to "citation statements only." Most users leave it on "both" and wonder why their results feel unfocused.
The model selection matters too. GPT-4-o-mini works great for general queries, but when you need complex analysis or nuanced reasoning, switching to GPT-4-o3-mini is like upgrading from a compact car to a high-performance vehicle: same road, completely different experience.
And here's a pro tip straight from the trenches: the publications volume setting isn't just a slider. One to ten publications works for fact-checking specific claims. Twenty to fifty handles focused reviews. But if you're doing gap analysis or investigating off-label uses? Crank it up to 100+ and let the AI show you patterns you didn't know existed.
Perhaps the most underutilized feature is the threaded conversation capability. Researchers tend to fire single shots at the AI, get disappointed, and give up. But these tools remember context within a session. Start broad, then narrow down.
Let’s break this down with another practical example.
Start with a broad prompt like: “Summarize major cardiovascular adverse events with SGLT2 inhibitors.” Then refine with a follow-up: “Break down the types of volume depletion events reported.” Finally, ask: “What are the opposing arguments about fracture risk increases?” Each query builds on the last, producing a comprehensive analysis that would take hours to compile manually.
The key is keeping related questions in the same thread. Jumping from SGLT2 inhibitors to CRISPR applications in the same conversation is like asking your cardiologist about your lawn mower mid-consultation: technically possible, but likely yielding unsatisfying results.
Here's something most users never discover: you can edit the AI's search strategy after it runs. Click "Search Strategy," then "Edit Searches," and suddenly you're not just using AI—you're directing it. Add Boolean operators (remember to use capitals: "AND," "OR," "NOT"). Remove searches that went off-track. Add new ones that target exactly what you need.
With just a few tweaks to the search terms, a mediocre response about GLP-1 agonists can be transformed into a focused, actionable analysis. You’re basically able to reach into your AI assistant’s “brain” and reorganizing its thoughts to better serve your needs.
Even with all these tools, things can still go wrong. The AI might get overly agreeable, missing contrasting evidence because LLMs are trained to please. Switch your reference ranking to "contrasting" and watch the AI surface all the studies that disagree with the consensus (you’ve just given your research assistant permission to play the contrarian role).
Or maybe you’re getting older information when you need the very latest research. Add a year filter, switch to recency ranking, and now you’re looking at last month’s publications instead of the last decade’s foundational studies.
Getting AI to work for research isn’t about learning to speak robot. Being extremely clear about what you want, how you want it, and what you don’t want cluttering up the results makes all the difference. Every vague prompt wastes time you could spend actually doing science.
The researchers who get the most out of these tools aren’t the ones with the flashiest prompts. They treat AI like a sophisticated instrument that needs careful calibration. You wouldn’t use a mass spectrometer without adjusting the settings, so why treat AI any differently?
AI should handle the heavy lifting, allowing you to concentrate on the thinking that really matters. The difference between a mediocre literature review and a comprehensive analysis can come down to a few settings tweaks and a well-structured prompt.
Next time you open Scite Assistant, don’t just type and hope. Plan your approach, use the settings, and guide your conversation strategically. Your future self (the one not drowning in irrelevant citations late at night) will thank you.