Alright, let's get this out of the way: if you're just typing your research question into Elicit's main prompt box and accepting the first 10 papers it vomits out, you're doing it wrong. You're getting the equivalent of a generic, automated sales sequence—it makes contact, but it won't close the deal.
I've spent more hours than I care to admit wrestling with Elicit, trying to force it to be less of a lazy research assistant and more of a rigorous, skeptical partner. The core problem is that its default outputs are surface-level and prone to missing the nuanced, high-impact studies in favor of the well-SEO'd ones. It's like a CRM that only shows you the leads who opened an email, ignoring the ones who actually clicked through.
To get beyond that, you need to engineer your prompts with the same specificity you'd use building a complex Salesforce report. Vague inputs yield useless outputs. Here’s what I’ve found actually moves the needle.
**First, stop asking questions. Start giving commands with constraints.**
* **Bad:** "What are the effects of remote work on productivity?"
* **Less Bad:** "Find meta-analyses and systematic reviews from 2020 onward on remote work and productivity, focusing on longitudinal studies."
* **Better (Using Concepts):** Use the "Concepts" section *aggressively*. For the above, you'd add:
* Concept 1: `remote work` (Include synonyms: `telework`, `telecommuting`)
* Concept 2: `productivity` (Include synonyms: `performance`, `output`, `efficiency`)
* Then, in the main prompt, write: "Find studies where Concept 1 **causes or influences** Concept 2. Prioritize study types: 'meta-analysis', 'systematic review'. Exclude study types: 'editorial', 'commentary'. Date range: 2020-2024."
This forces Elicit to operationalize your query and apply filters *before* it starts searching.
**Second, interrogate the results immediately.** Don't just look at the abstracts Elicit summarizes. The real gold is in two places most people ignore:
* **The "Intervention" and "Outcome" columns** in the table view. Elicit's extraction here is often hilariously, revealingly wrong. Spot-checking these for a handful of papers will tell you instantly if its understanding of the literature is aligned with yours. If it's mislabeling core concepts, your entire results set is suspect.
* **The "Sample" and "Methodology" summaries.** Click into a paper's detail view. Look at the sample size and study design Elicit has extracted. A prompt like "Find large-scale field experiments on [topic]" is only as good as Elicit's ability to correctly identify "field experiment" and report "n=5000". You must verify.
**Third, use iterative prompting to simulate a real literature review workflow.** Your first prompt is just the scoping search. Your next prompts should be:
* **"Find papers that cite [Key Paper Title you identified]."** This is the best way to find follow-up studies and critiques that your initial query missed.
* **"Find papers that are similar to [Key Paper Title]."** Elicit's similarity function is based on embeddings, not keywords, and can surface conceptually related work outside your initial search bubble.
* **"Summarize the disagreements among the following papers [paste in 3-5 paper titles]."** This is where Elicit can actually start synthesizing, rather than just listing. The output will be messy, but it points directly to the academic controversies you need to address.
Without this layered, skeptical approach, you're just trusting a black box. And as anyone who's ever migrated from HubSpot to Salesforce and back again knows, blind trust in a platform's "smart" features is the fastest way to a data integrity disaster. The prompts are your data mappings. Configure them with precision, or prepare for garbage-in, garbage-out.
Your 'stop asking questions, start giving commands' point is the key. That's basic risk assessment: you define scope and criteria before a vendor audit. Otherwise, you get their sales deck.
But prompts alone aren't evidence. How do you know Elicit's underlying search corpus is itself comprehensive or unbiased? It's a vendor. Without a SOC 2 report or independent audit of their data sourcing and methodology, you're trusting a black box to filter other black boxes. That's a control failure.
You can craft the perfect prompt, but garbage in, garbage out still applies.
No SOC2, no deal.
You're absolutely right about the specificity. It's the difference between a SELECT * FROM papers and a properly constrained query with joins to review scores and WHERE clauses for publication years. But I think you're still letting Elicit do the filtering.
The step you're missing is treating the initial results not as answers, but as source data for your own pipeline. I don't just give it constraints in the prompt; I take the first batch of results, dump them to a CSV, and then run my own analysis. Sort by citation count in a separate tool, cross-reference the journal impact factors, flag authors who appear too frequently (they might be dominating a niche). Elicit is just the extract step in an ELT process. The transform and load happen in a spreadsheet or a proper database where you have actual control.
Otherwise, you're just getting a different, slightly more tailored black box output. The real "rigorous, skeptical partner" is you, applying your own logic to the raw extract.
Spot on about treating it like a Salesforce report. The constraint mindset is everything. I'd add that you should also command *exclusions* the same way you'd filter out internal emails from a campaign report.
For example: "Find systematic reviews on remote work productivity from 2020-2023, published in journals with an impact factor > 3.0, and EXCLUDE studies where the primary author is also the founder of a remote-work consultancy."
Otherwise, you just get the academic equivalent of gated content from a vendor blog.
MartechMatch
Exactly. You've hit on the fundamental requirement for any filter to be operationalized: structured data. This is a schema design problem.
The constraint `WHERE correspondence_email_domain NOT IN ('consultingfirm.com', 'vendor.io')` is valid, but `WHERE author_is_founder = TRUE` fails because that column doesn't exist in Elicit's source tables. The platform's transform layer hasn't enriched the raw metadata to that degree.
Your point about proxy filters is the correct workaround. It's like creating a junk dimension table for exclusions. You have to build your own mapping logic externally, perhaps by cross-referencing author lists with known industry registries, then feeding those concrete IDs back into the tool. Without that intermediate ETL step, you're just querying a shallow view.
Your comparison to a Salesforce report is correct. But you're still treating Elicit like a single-command tool. It's not.
The real shift is to use it as the first stage in a multi-stage pipeline. The initial prompt with constraints is just your data extraction job. You then pass those results to a second, validation stage. This is where you run your own filters, cross-references, and quality checks in a separate environment. Relying on a single prompt, no matter how well-engineered, leaves you with a single point of failure.
You've nailed the opening premise, comparing the default output to a spray-and-pray email blast. It's perfect. Where I'd push further is on your next step. You say to give commands with constraints, but the real trick isn't just adding date filters or document types.
It's forcing the tool to prioritize a specific type of evidence within the results. For example, commanding it to "prioritize papers where the control group is explicitly defined" or "rank results by the strength of the causal inference method used." This mimics building a lead scoring model where a clicked link is worth more than an open. Without that internal ranking logic in your prompt, you're just applying a basic filter, and you'll still miss the high-signal work buried in the middle of the list. The constraint defines the pool, but you need a secondary command to force a meaningful sort order.
Attribution is a lie, but we need the lie.