Expected Parrot: Tools for AI-Powered Research
Expected Parrot delivers powerful tools for conducting research with human and artificial intelligences.
This page provides documentation for the Expected Parrot Domain-Specific Language (EDSL), a Python package for performing research with AI agents and language models, and Coop, a platform for creating, storing and sharing AI-based research projects.
Create a Coop account to access special features, storage and collaboration tools. Learn more about how it works and start exploring.
Key features
- Simplified access to hundreds of models
A single API key lets you conduct research with many popular models at once. Learn more.
- Collaboration made easy
Use Coop to create, store, and share your research projects seamlessly.
- Data integrations
Easily import, analyze and extend many types of data. Learn more.
- Hybrid human-AI surveys
Collect responses from both humans and AI. Learn more.
- Built-in analysis tools
Analyze results with built-in methods. Learn more.
Use cases
Our tools simplify survey creation, experiment execution and response analysis. Common use cases include:
- Data labeling
Use AI to answer qualitative and quantitative questions about your data, and extract insights. See examples.
- Market research
Gather insights on consumer preferences, behaviors and trends. Simulate customer personas with AI agents and analyze their responses. See examples.
- User experience research
Assess user satisfaction, usability and engagement. Use AI agents to simulate user profiles and analyze their feedback on products or services. See examples.
- Integrate human and AI data
Combine human responses with AI-generated responses to create richer datasets. See examples.
- Analyze survey data
Analyze survey data with built-in methods. Simulate follow-up interviews with respondents. See examples.
- Social science research
Explore hypotheses, gather qualitative or quantitative data and generate new data using AI.
For more on EDSL’s key features and use cases, visit the Overview section.
Getting started
Technical setup:
Install the EDSL package. See Installation instructions.
Log in to Coop to access special features for working with AI agents and language models, storage and collaboration tools.
Choose how to access language models:
Remote: Use your Expected Parrot API key to access models at Coop. See instructions on activating Remote Inference and purchasing credits.
Local: Use your own API keys for models to use on your own machine. See instructions on storing API Keys.
Support and resources:
Explore a Starter Tutorial, how-to guides and notebooks. See tips on using EDSL effectively.
Join our Discord channel to ask questions and chat with other users!
Researchers
Are you using EDSL for a research project? Send an email to info@expectedparrot.com and we’ll give you credits to run your project!
Introduction
Overview: An overview of the purpose, concepts and goals of the EDSL package.
Whitepaper: A whitepaper about the EDSL package (in progress).
Citation: How to cite the package in your work.
Papers: Research papers and articles that use or cite EDSL.
Core Concepts
Questions: Learn about different question types and applications.
Scenarios: Explore how questions can be dynamically parameterized for tasks like data labeling.
Surveys: Construct surveys with rules and conditions.
Agents: Design AI agents with relevant traits to respond to surveys.
Language Models: Select language models to generate results.
Working with Results
Results: Access built-in methods for analyzing survey results as datasets.
Caching LLM Calls: Learn about caching and sharing results.
Exceptions & Debugging: Identify and handle exceptions in running surveys.
Token usage: Monitor token limits and usage for language models.
Coop
Coop is a platform for creating, storing and sharing AI-based research. It is fully integrated with EDSL and provides access to special features for working with AI agents and language models, storage and collaboration tools, including:
Credits: Get credits to use remote inference.
Remote Inference: Run surveys with any available models on the Expected Parrot server.
Remote Caching: Automatically store results and API calls on the Expected Parrot server.
Survey Builder: Design and launch hybrid human-AI surveys.
Notebooks & Colab Notebooks: Post .ipynb and .py files to the Coop.
File Store: Store and share data files for use in EDSL projects.
How-to Guides & Notebooks
Examples of special methods and use cases for EDSL, including:
Data labeling
Data cleaning
Analyzing survey results
Adding data to surveys from CSVs, PDFs, images and other sources
Conducting agent conversations
Converting surveys into EDSL
Cognitive testing
Research methods
Links
Download the current version of EDSL at PyPI.
Get the latest EDSL updates at GitHub.
Create a Coop account.
Join the Discord channel.
Send an email: info@expectedparrot.com