Yale's AI Tools and Resources
Explore AI tools available to students, faculty, and staff through comprehensive tables detailing options, access methods, and supported data types. Start experimenting to discover what works best for your needs.
| Description | Free At Yale? | |
|---|---|---|
| Azure AI | A comprehensive framework of AI tools created by Microsoft that provides a suite of business operational support. Offers access to over 11,000 foundational, open-source, reasoning, multimodal, and industry-specific models from providers such as DeepSeek, Meta, NVIDIA, and more. Includes standard code assistance tools with compatibility across numerous IDEs and an array of languages, integrated with Microsoft Agent Framework, LangChain, CrewAI, and LlamaIndex. Provides pre-built AI agents and features, supports development of custom MCP tools, and includes usage monitoring and governance for enhanced security, cost management, and harmful content reduction. | No |
| Claude Code | An AI tool from the Claude series designed by Anthropic and trained using Constitutional AI, offering models like Opus, Sonnet, and Haiku. Integrates with Command-Line Applications such as terminal, and IDEs including VS Code and JetBrains to support advanced code analysis, debugging, and feature implementation. Connects with other command-line tools used in software development including deployment, databases, monitoring, and version control. Securely runs locally to interact with model Application Programming Interfaces (APIs) without a backend or remote code index. Maintains user control by requesting modification approval, adapts to user-defined standards for coding practices, and builds on established Software Development Kit (SDK) and GitHub Actions. | No |
| Codex | An AI-assisted software development tool developed by OpenAI and based on their frontier coding models and is a descendant of GPT-3. Accessible through their CLI, GitHub, and select IDEs, providing coding support for routine to complex tasks. Proposes end-to-end solutions with agent prompts to build features, resolve complex refactors, handle migration tasks, interpret code, and write documentation. Agents can work in parallel across projects or execute unprompted automations of routine tasks such as triage, alert monitoring, and Continuous Integration and Continuous Deployment (CI/CD). | No |
| Cursor | A generative and agentic IDE plugin that enhances software development by improving code comprehension through synthesis of project information, detailed code summaries, and automated documentation creation. Includes standard code support tools such as code completion, available through its desktop IDE based on VS Code and a Command-Line Interface (CLI). Designed specifically to support software development workflows and tasks including planning, designing, debugging, Git checkpoints, and team collaboration. Supports custom domain knowledge through training, MCP integration, and plugins such as Figma, Slack, and GitHub. Users can access major models including Claude, Gemini, and GPT. | No |
| GitHub Copilot | A generative AI model developed by GitHub, OpenAI, and Microsoft, trained on publicly available sources including public repositories on GitHub and based on the GPT-3 architecture. Installable across numerous Integrated Development Environments (IDEs) and supporting an array of programming languages. Provides code completion, management agents for generating audits, and custom Model Context Protocol (MCP) integration. Can be trained on specific projects to become a tailored expert. | No |
| Replit | Based on OpenAI models like GPT-3, Replit offers a comprehensive platform that enables users to code and develop software projects, including featured tools like Ghostwriter. The platform provides various tools specifically designed for different purposes such as app development, website creation, and database integration with third-party services. Replit is created with a focus on beginners who may have little to no prior experience with programming languages or developing products as code projects. It enhances the learning experience by offering detailed explanations and tips throughout the development process. For direct coding, Replit includes standard AI-assisted features such as autocompletion and debugging assistance, making it a valuable tool for both novice and experienced developers alike. | No |
| Description | Free At Yale? | |
|---|---|---|
| DALL-E | A foundational transformer model using neural networks developed by OpenAI that translates text prompts into high-fidelity images. This 12-billion parameter version of GPT-3 is trained on text-image pairs, generating diverse responses including plausible combinations of unrelated concepts. Capabilities include controlling attributes, spatially orienting multiple objects, visualizing perspectives, and inferring contextual details from simple prompts. It discerns visual elements with temporal, geographical, and zoomed perspectives. OpenAI implements safety features to prevent harmful content, deepfakes through detection algorithms, and biased content. Recent iterations prevent generating content in the style of living artists and allow artists to opt out of training. | Yes |
| Midjourney | Midjourney is a generative image model and service (delivered primarily through Discord and, more recently, web interfaces) that transforms natural-language prompts into high-quality images and videos. Its models are trained using a mix of publicly available data, third-party data, and Midjourney user data, along with internally labeled and generated data used to guide model tuning. It is known for producing stylized, cohesive compositions and for offering iterative “variation” and “upscale” workflows that let users refine outputs through successive generations. Users can guide results with parameters that influence aspect ratio, stylization, randomness, and reference behavior (for example, using an input image to steer composition or aesthetics), making it useful for concept art, mood boards, product ideation, and visual exploration. Like other text-to-image systems, it can reflect bias, produce inaccuracies, and echo training-data patterns; the platform enforces content rules and moderation to reduce harmful or abusive use. Usage rights and privacy depend on Midjourney’s terms and subscription tier. | No |
| Nano Banana | Nano Banana 2 is an AI image-generation service that converts natural-language prompts (and, where supported, image references) into synthetic images. It is designed for rapid visual ideation, enabling users to iterate through multiple generations and refine outputs via prompt adjustments and model controls such as style/strength, aspect ratio, and variation settings. Typical capabilities include creating scenes with multiple elements, adjusting visual attributes (color, lighting, composition), and producing images in a range of illustrative or photoreal styles depending on the prompt and presets; it can also demonstrate limited cultural-context awareness by picking up on geographically, temporally, or culturally specific visual cues implied by a prompt. It is often described as offering industry-leading character consistency, helping users keep a subject’s identity and key features more stable across multiple generations. Like other text-to-image systems, outputs can be inconsistent, may contain factual or anatomical errors, and can reflect biases or patterns present in training data. Responsible-use controls and content policies vary by platform and deployment; users should review the service’s terms for permitted use, licensing, and any restrictions around impersonation, copyrighted styles, or sensitive content. | No |
| Description | Free At Yale? | |
|---|---|---|
| ChatGPT Deep research | ChatGPT Deep Research is a research workflow designed for complex, high-stakes questions that require directed search, synthesis, and verification. It prioritizes trusted, authenticated sources—such as connected private files, enterprise apps, paid datasets, and administrator-approved URLs—and produces structured reports with citations so you can quickly trace claims back to evidence. Users can control the process end to end by editing the research plan, refining the scope, updating sources, or interrupting at any point while tracking progress in real time, enabling ~30‑minute reports that would otherwise take hours or days. Built for long, technical context, it supports structured reasoning across lengthy documents and multi-source comparisons for tasks like market analysis, regulatory review, literature comparison, and technical synthesis. In enterprise environments, permissions and source controls are administrator-managed to govern app access and help keep sensitive data within the appropriate teams. | No |
| Consensus | An AI-powered search engine designed to assist with directed searches and synthesis of published papers. It focuses on providing an accurate and accessible tool trusted by academic researchers and university students. The search engine sources from more than 250 million cited, peer-reviewed literature, including full-text content from leading publishers. AI responses are annotated with citations to real literature papers, allowing users to narrow matches to meet specific inclusion criteria (e.g., timeframes and study design) or compare published results on a given topic. | No |
| Elicit | An AI-powered search engine designed to assist with directed searches and the synthesis of published papers. It is utilized in both academic and industry settings to curate and synthesize search results from over 138 million academic papers and 545,000 clinical trials, with content continually expanding. The engine uses semantic search approaches, alleviating the need for exact keyword searches. It allows users to fine-tune results, such as changing the papers used in reports and rerunning reports to integrate recent updates. AI responses are cited to ensure traceability and highlight the specific portions of papers used to generate those responses. The tool is user-oriented, supporting automated screening and data extraction, cataloging results for later reference, and setting up alerts for new research releases. | No |
| Gemini Deep Research | Gemini Deep Research is a research workflow designed for complex questions that require directed search, synthesis, and verification across many sources. It emphasizes using trusted sources and producing structured, citation-backed reports so readers can quickly trace claims to evidence. Users can steer the process by refining the research plan and scope, adjusting what sources are used, and iterating as findings emerge, with progress visible as the system works. It is built to handle long, technical context for tasks like market and competitive analysis, regulatory and policy review, literature comparison, and synthesizing lengthy technical documents. In organizational deployments, access controls and permissions can be managed to govern which connected apps and data sources are available and to help keep sensitive information within approved boundaries. | No |
| Research Rabbit | A free AI-powered search engine designed for topic-based searches that facilitates the discovery of related papers. The search engine sources from more than 270 million academic papers and is used by researchers and institutions worldwide. Its algorithms learn from your reading and searching patterns to tailor results to your research interests, improving recommendations with each use. Notably, search results include a graphical visualization showing how paper topics are connected. | No |
| Manus | Manus is an AI research-and-execution agent designed to take complex goals and carry them through end to end—planning the work, gathering information from approved sources, and producing structured, checkable outputs. It supports user control throughout the process: you can adjust the plan, narrow or expand scope, swap inputs, or stop and redirect at any time while monitoring progress as tasks run. Manus is built for multi-step work such as market scanning, document and policy review, competitive comparisons, and synthesizing long technical materials into briefs, tables, and action-oriented deliverables. In team or enterprise settings, it can be deployed with permissioning and source controls so administrators can govern which tools, apps, and repositories it can access and help ensure sensitive data is handled within the right boundaries. | No |
| Scite | An AI-powered search engine for directed searches and synthesis of published papers. It has indexed 1.4B+ citations, partners with 30+ publishers, and includes paywalled content. The platform serves researchers, students, publishers, universities, librarians, and industry professionals worldwide. The SmartCitation feature evaluates whether citations support or contradict claims and flags retracted or disputed publications. This is available via app and browser extensions (Chrome, Firefox, Safari) that show how articles are cited online. Publishers can track how their work is mentioned, and the platform generates visualizations of citation networks. | No |
| Semantic Scholar | A free AI-powered search engine designed for directed search, synthesis, and effective reading of published papers. Search over 214 million papers across all fields of science and filter by journal, conference, topic, or “Highly Influential Citations” (identified by a machine-learning model that assesses citation count and context). Features include an in-app citation generator (MLA, APA, Chicago, BibTeX), organized libraries that generate personalized search recommendations based on user ratings, and alerts for new citations to specific papers or authors. Two notable features enhance reading: Ask This Paper provides AI-generated answers to questions about a given paper, while Semantic Reader offers content highlighting for easier skimming, personalized citation indicators for papers in your library, and TLDR (Too Long; Didn’t Read) summaries of cited works for context. The Semantic Reader Project is open-source to support development of new literature search tools. | No |
| Undermind | An AI-powered, source-grounded research assistant that turns a broad question into a structured plan, searches and evaluates the literature (including citation trails), and synthesizes traceable summaries. It’s geared toward assessing novelty (whether an idea has been explored or may be genuinely new), scoping complex, niche topics before you invest significant time, and surfacing cross-disciplinary connections that can inspire new approaches. It also helps identify gaps and emerging trends by highlighting unanswered questions, and it reduces research bottlenecks by finding relevant methods, datasets, and prior solutions linked to your problem. For high-stakes decisions, its takeaways should still be validated against the cited underlying papers and references. | No |
| Description | Free At Yale? | |
|---|---|---|
| ChatGPT | A large language model (LLM) developed by OpenAI that interacts with users through open-ended dialogue and serves as the core model in specialized tools deployed by OpenAI and other developers. It is trained using Reinforcement Learning from Human Feedback (RLHF), similar to InstructGPT, with supervised fine-tuning and output ranking. Notable limitations include hallucinations, sensitivity to prompt variation, and inferring user meaning without asking for clarity. OpenAI continually works to improve content appropriateness, reduce biased results, and enhance usage controls with each release. They offer various tools and models designed for specific applications and domains (research, business, coding, etc.). | Yes |
| Claude | An AI tool from the Claude series developed by Anthropic using Constitutional AI training, offering models like Opus, Sonnet, and Haiku with varying performance features that serve as the foundation for tools from Anthropic and other developers. Trained and fine-tuned using RLHF and supervised training aligned with Anthropic’s Constitutional AI standards for helpful, honest, and harmless AI. Uses retrieval augmentation to combine trained outputs with external data. Limitations include hallucinations and latency, though stringent safeguards are implemented aligned with Constitutional AI principles. They offer various tools and models designed for specific applications and domains (research, business, coding, etc.) with integrations in workflow tools like IDEs, Microsoft 365, and web browsers. | Yes |
| Gemini | A multimodal LLM (text, audio, images, and more) hybrid tool developed by Google for open-ended dialogue that serves as the foundation for specialized tools from Google and other developers. Trained on publicly available data filtered for appropriateness and fine-tuned with RLHF and supervised training. Uses retrieval augmentation to combine trained outputs with external data. Limitations include hallucinations, persona projection, glitches with nonsensical prompts, and presenting narrow options rather than comprehensive synthesis. Google continually works to improve content appropriateness, reduce bias, and enhance usage controls with each release. They offer various tools and models designed for specific applications and domains (creative inspiration, media generation, research, business, coding, etc.). | Yes |
| Llama | A family of open‑weights large language models (primarily text-focused, with some variants supporting vision) developed by Meta for general-purpose language understanding and generation, widely used as a foundation for fine-tuned assistants and domain-specific tools by companies and researchers. Trained on large-scale datasets and typically refined with RLHF/RLAIF-style alignment, and commonly deployed with retrieval augmentation so responses can be grounded in external documents, databases, or enterprise knowledge. Limitations include hallucinations, uneven performance on niche or rapidly changing facts, sensitivity to prompt phrasing, and the risk of producing biased or unsafe outputs if not properly tuned and constrained. Because Llama models are frequently self-hosted or integrated into custom stacks, quality and safety depend heavily on the specific version, fine-tuning, guardrails, and data/permission controls implemented by the deploying organization. | No |