Most users ask vague questions and get vague summaries. To get a specific data table, you need to treat Elicit like a structured query engine.
Your question must specify the exact output format and the variables to compare. Ambiguity returns literature reviews; precision returns tables.
Example of a bad vs. good prompt:
**Bad:** "What are the effects of zero-trust?"
**Good:** "For the concepts 'zero-trust network access,' 'software-defined perimeter,' and 'beyondcorp,' extract a table comparing: primary author (organization), key technology components, deployment model (agent-based/gateway), and year of primary reference (2015-2023)."
Key principles:
* List the exact column headers you want.
* Define the entities (models, tools, concepts) to compare.
* Constrain the scope (e.g., publication years, specific vendors).
-dk
Trust but verify, then don't trust.
I'm an analytics engineer at a mid-market SaaS company. Our stack is Snowflake, dbt, and Fivetran, and in production, I run all our analyst-facing SQL workbenches, so I evaluate these tools constantly.
The right tool depends entirely on whether you're asking analysts or business users to generate these structured tables. Here's the breakdown:
1. **Target User & Skill Floor:** For a business user, ThoughtSpot's search is viable; an analyst just needs a decent SQL IDE. ThoughtSpot requires a modeled star schema and specific naming conventions to work reliably. A pure SQL tool like Mode requires the user to already know the schema and SQL.
2. **Real Pricing & Model:** Mode starts around $50/user/month for its lowest commercial tier, but it's workspace-based, so you pay for editors, not viewers. ThoughtSpot is often $80+/user/month and leans on annual commitments. The hidden cost for ThoughtSpot is the semantic modeling layer upkeep.
3. **Deployment & Integration Effort:** Adding a SQL-based tool is mostly about connecting to your warehouse and managing user SSO. Adding a tool like ThoughtSpot is a multi-week project: you must build and maintain a dedicated BI layer, which adds another semantic model to manage alongside your dbt project.
4. **Performance & Where It Breaks:** In my environment, a complex ad-hoc query in Mode or Superset runs directly against Snowflake, so performance is your warehouse's responsibility. ThoughtSpot must translate a search phrase into SQL, and its generated SQL can be inefficient, sometimes hitting 2-3x slower on complex filters than a hand-written query. It clearly wins on simple, repetitive questions against pre-defined models.
I'd recommend a SQL workbench like Mode or Superset for any team where the "question asker" is comfortable with basic SQL. If your users are non-technical and your data model is stable and well-documented, then a tool like ThoughtSpot is the only viable path. To make a clean call, tell us the job title of the person asking the questions and how often your core data tables change.