Hey everyone! I've been using Elicit heavily for academic literature reviews in my PhD work, and the structured extraction of claims and methodologies is fantastic. But I'm curious if anyone has repurposed it for commercial market research or competitive analysis.
I gave it a quick test run last week. Instead of asking about "the effects of climate change on crop yields," I tried prompts like:
* "What are the main challenges for SMBs adopting AI-powered CRM tools?"
* "List key value propositions of the top 3 project management SaaS platforms."
The results were... mixed. It surfaced some relevant whitepapers and industry reports, but I noticed a few things:
* **Source Bias:** The corpus still leans heavily toward peer-reviewed journals. For market trends, you really need recent blog posts, analyst reports (Gartner, Forrester), and maybe even curated news.
* **Extraction Limits:** The template for extracting "Interventions" and "Outcomes" is perfect for clinical trials, but less so for extracting "Pricing Models" or "Target Customer Segments" from a G2 review.
* **Date Sensitivity:** Market landscapes shift quarterly. Elicit's default sorting by relevance sometimes prioritizes seminal but outdated academic papers over a crucial 2023 market analysis.
Has anyone built a workflow around this? I'm thinking:
1. Using Elicit for an initial "broad sweep" to find foundational industry frameworks or long-term trend studies.
2. Then feeding those key terms into a more news-focused tool (maybe with a custom Python scraper) to get the fresh takes.
3. Manually curating the final synthesis.
If you've tried something similar, I'd love to hear:
* What prompt engineering tricks worked for you?
* Did you use the API to blend Elicit's output with other data sources?
* Any clever uses of the "Summary of top papers" feature for business contexts?
Maybe we can crowdsource some effective non-academic query patterns!
Clean code is not an option, it's a sanity measure.
Interesting test! That source bias is a real blocker. It's designed for the arXiv/PubMed corpus, not Gartner's magic quadrant.
I wonder if you could get further by framing your prompts as "academic" questions. Maybe "What does academic research say about barriers to technology adoption in small and medium enterprises?" might trick it into finding more foundational studies you could then map to your market.
Have you tried any other tools specifically built for scraping and analyzing that kind of commercial content?
git push and pray
Yeah, the source bias and date sensitivity you flagged are the real killers for commercial use. I tried something similar for a cloud migration cost analysis.
Elicit pulled up great papers on TCO models from 2018, but the actual pricing for AWS services or committed use discounts changes constantly. I needed current blogs and documentation, not peer-reviewed theory.
Did you find a workaround, or did you abandon it for a different tool? The structured extraction is tempting, but what's the actual ROI if the data is stale or from the wrong corpus?
Ask me about hidden egress costs.
That's a perfect example of using a tool exactly as it was built, and then being surprised when it doesn't work for something entirely different. It's an academic literature search engine, not a market intelligence AI.
The real question is why you'd even want to force-fit it. The extraction templates are hard-coded for academic concepts. You're not going to get a "Pricing Model" column out of a system that thinks in terms of "Participant Count" and "Statistical Significance."
You're seeing source bias because that's the entire point of its corpus. The ROI on this workaround is negative; you'll spend more time trying to trick the prompt and clean the data than just using a tool meant for commercial scraping.
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