What is Elicit AI, pros and cons, use cases and prices
Elicit AI is an AI-powered research assistant designed to automate and enhance various aspects of the research workflow, especially in the realm of literature reviews. It utilizes advanced language models to efficiently find relevant papers, summarize key findings, and extract crucial information tailored to specific research questions.
Pros of Elicit AI:
- Time-saving: Automates parts of the literature review process, helping researchers save significant time and effort.
- Relevance Discovery: Capable of finding pertinent papers beyond mere keyword matches.
- Perspective Broadening: Offers different perspectives and potential new questions for exploration.
- Information Extraction: Efficiently extracts key data from papers, like methods, results, and conclusions.
- Versatility: Assists in tasks such as brainstorming, summarization, and text classification.
Cons of Elicit AI:
- Not a Substitute for Human Judgment: While it aids research, it cannot replace critical thinking and human evaluation of paper quality and validity.
- Limitations in Complexity: May struggle with complex or deeply analytical questions.
- Scope of Sources: Might not cover all relevant sources or databases, necessitating supplemental tools.
- Language Constraints: Primarily supports English, potentially requiring translation for other languages.
- AI Model Limitations: There may be errors or limitations due to the AI models used.
Pricing and Plans:
- Free Trial: Elicit offers a free trial with an initial allocation of 5,000 credits for tasks like paper searches and summarization.
- Pay-as-You-Go: Users can purchase additional credits at $1 per 1,000 credits. This model is cost-effective and adaptable to varying research needs.
- Enterprise Plan: Custom pricing and features for larger organizations, including tailored workflows and data source integrations.
- Plus Subscription: Priced at $10 per month or $120 per year, offering 12,000 credits per month and additional features like the ability to export results.
Use Cases:
- Speeding up literature reviews by quickly summarizing papers and extracting relevant data.
- Automating systematic reviews and meta-analyses, allowing more focus on analysis and interpretation.
- Learning about new domains, particularly effective in empirical fields like biomedicine and machine learning.
