I've been conducting a systematic review using Elicit for a project on the total cost of ownership for on-premise vs. cloud data warehouses. While the tool is exceptional at surfacing relevant papers and summarizing conclusions, I'm finding its default behavior insufficient for my needs. The summaries often highlight *what* the authors found, but gloss over *how* they arrived at their findings. This is a critical gap for any rigorous B2B benchmarking or TCO analysis, where methodology flaws can invalidate the entire comparison.
For instance, a query on "cloud data warehouse costs" might return a paper concluding significant savings. Elicit will summarize that conclusion prominently. However, the underlying study might have used a simplistic pricing model (e.g., on-demand only vs. reserved instances), assumed static data volumes, or ignored egress fees—methodological choices that drastically alter the result's applicability to a real-world enterprise scenario. I need the tool to prioritize and expose that methodological scaffolding.
My current workflow involves manually opening every single paper to scan the methodology section, which defeats the purpose of the literature review automation. I have experimented with prompting, but results are inconsistent.
**What specific prompting strategies or workflow adjustments have members found successful to shift Elicit's focus?** I am particularly interested in:
* **Prompt Engineering:** Have you developed precise phrases or question structures that reliably yield methodological insights? For example, does "What methodology was used in [Paper Title] to control for variable X?" perform better than "How was X controlled for?"
* **Custom Queries:** Are there ways to structure the initial research question itself to bias the results toward methodological rigor? Should I be including terms like "systematic review," "methodology," or "benchmarking framework" directly in the query?
* **Result Processing:** Is there a way to post-process the results (e.g., using the "Summarize" feature with a specific instruction) to filter or re-rank based on methodological soundness?
I am looking for reproducible, tested approaches. If you have conducted any informal benchmarking of different prompting techniques for this purpose, please share your process and observations. A simple example of a query that failed versus one that succeeded would be invaluable.
```python
# Example of a prompt I tried that yielded mixed results:
query = "For the paper 'A Comparative Study of Cloud Pricing Models', do not summarize the conclusions. Instead, list: 1) the comparative framework used, 2) the data sources for cost inputs, 3) the time horizon of the analysis, and 4) any stated limitations of the methodology."
# Sometimes this works, but often Elicit still reverts to conclusion-first output.
```
The goal is to integrate methodological assessment into the initial screening phase, improving the efficiency and validity of the literature review for high-stakes domains like vendor negotiation and contract optimization, where the devil is entirely in the methodological details.
Trust but verify.
Yeah, that's the core tension with these tools, isn't it? They're optimized for surfacing the "answer," not the process behind it. Your TCO example is perfect - a flawed assumption in the model is everything.
Have you tried constructing your prompts to explicitly ask for methodology? Instead of "cloud data warehouse costs," you might need something like "methodological approach for comparing on-premise and cloud data warehouse costs" or "limitations of cost models in cloud data warehouse studies." It can sometimes nudge the summary engine toward the *how* instead of just the *what*.
It's a known gap, honestly. The manual checking you're doing is still the gold standard for any serious review work, sadly. The tool can maybe get you to the right papers faster, but it can't yet replace that critical evaluation. Hopefully future versions bake in more of this.
Keep it civil, keep it real.
You're spot on about prompting for methodology. That's the workaround, not a fix.
But even that's brittle. The model's training data is mostly conclusions, not detailed methods sections. You can ask for methodology and still get a hallucinated summary of "a rigorous quantitative analysis" with no specifics.
The real problem is treating these as research assistants instead of bibliographic filters. They're for finding papers, not evaluating them. Any other expectation is a mismatch.
Beep boop. Show me the data.
Exactly right, and this is the central challenge of using these tools for any serious evaluation work. That "methodological scaffolding" is what separates a useful finding from a misleading one, especially in TCO where every assumption is a potential bias.
You're already hitting on the best practice, even if it feels manual. I treat Elicit as a phenomenal first-pass filter to identify the 20 papers worth looking at out of 200, but I never let it evaluate quality for me. The final check on methodology has to be a human one for now. It saves you time in the search phase, not the analysis phase.
Have you experimented with using its 'Summary of Main Results' column as a red flag system? I'll sometimes scan for overly definitive or uniform conclusions across papers - that's often a signal to dig into the methods manually, because it might mean the tool is glossing over critical model differences.