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Harness AI to streamline UX research and unlock actionable insights with UserTesting AI.

How UserTesting can help you:

Increases team efficiency by automating repetitive tasks.
Accelerates time to insights through AI-powered analysis of user data.
Amplifies business impact by making research accessible and actionable across teams.

Why choose UserTesting: Key features

AI Insight summaries for quick understanding of key findings.
Friction detection to identify and improve user experience issues.
Sentiment analysis to gauge user reactions and emotions.
Domain-specific datasets and machine learning models tailored for UX research.

Who should choose UserTesting:

UX/UI Designers seeking deeper insights into user behavior.
Product Managers aiming for accelerated product development cycles.
Marketing teams looking for customer feedback to drive strategy.
Business Executives focused on leveraging user research for strategic decisions.

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https://www.usertesting.com/platform/AI

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March 2024

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A practical guide to AI integration

AI transforms product development: prioritizing user needs and avoiding premature optimization The use of AI in product development is revolutionizing the way companies drive innovation and respond to user needs. In the face of these massive changes, today's technology leaders must ensure that their efforts have a real impact and are not just empty words. A solid foundation consists of a clear understanding of customer needs, market dynamics and the technology landscape. This requires a balanced mix of user-centricity, deliberately limiting AI optimization to relevant areas, and ensuring that the underlying requirements and processes are ready for the integration of AI tools. In this way, organizations can create innovative, sustainable and user-friendly solutions that unlock the true value of AI in product innovation. AI is all the rage right now, but unlike recent hype cycles, the natural language and reasoning capabilities of large language models (LLMs) have the potential for broad impact across a wide range of products. But adding AI to a well-functioning product just for the sake of being on-trend only confuses users. Before implementing AI, companies should ask themselves whether AI actually addresses problems or gaps. User evaluation is key. How can AI improve the employee experience without losing sight of the product strategy? Interactions that would benefit from a natural language interface, or manual workflows that could be streamlined, may also be important. A familiar example: when searching the online platform AirBnB, users filter options based on criteria like price or number of bedrooms. Simply replacing a straightforward filter with users typing a natural language question doesn't unlock new capabilities and creates a more cumbersome flow with a higher risk of unexpected results. Still, even with multiple filters, it's not always easy to find what you're looking for. Modeling the long tail of personal criteria as effective filters is difficult. The ability of AI to understand the nuances of natural language can make all the difference. With AI-powered search, there are no limits to personalization. At the same time, fast, meaningful and functional filters should not be sacrificed. While it is relatively easy to create a compelling demo with new AI technologies, it is challenging to develop a useful product. Building a step-by-step process allows you to learn from user feedback and is critical to creating a valuable and compelling experience. One of the exciting facets of this latest wave of AI is its ability to be hyper customized. What used to require human-level comprehension of nuance and intent, can now be digitized and made accessible at scale. The fascination with the technology should not get in the way of knowing everything about practical product development. While fine-tuning a bespoke model might seem enticing, it's a form of premature optimization that locks in a set of choices before the correct product fit is found. Fine-tuning an AI model prematurely slows down the rate of iteration and increases the maintenance costs, ultimately hindering innovation velocity. So how do you create a custom experience? It's all about the prompt. The prompt is a great place to set the tone for the interaction -- confidence, cultural or industry adjustments, the brand voice, and more. It should communicate any proprietary information that the model should use. The prompt should also summarize the context that needs to be provided to new employees to complete the task. This approach provides the flexibility to incrementally improve and adapt as both the underlying technology and the understanding of how to use it evolve. The degree of sophistication in structuring the task is ultimately a key differentiator for a product. AI models are like black boxes - a query leads to an answer. Even small changes can lead to massive changes in quality. Early implementation of a quality assurance process allows for effective evaluation of improvements and rapid detection of deterioration. To keep up with the pace of change in AI, a team needs to be able to evolve quickly. A solid foundation starts with building an AI platform that paves the way for developers and enables both rapid iterations and consistency across the product. Consideration should also be given to standardizing on approved vendors and models, a basic query framework, an approach to quality testing, and basic patterns and functions for extracting relevant data from common data sources to serve as context in the query. While there can be many challenges in simplifying an AI platform, there should not be too much focus on centralization. It's not about the technology, it's about how it's integrated into the product. Teams responsible for a specific aspect of a product are best placed to identify and optimise appropriate use cases. Therefore, all members of a product development team should be able to use AI successfully in their respective areas. We list the best productivity tool.

TechRadar

Mon, 22 Jul, 8:02 AM UTC

MuukTest is putting GenAI at the center of software QA testing | TechCrunch

Every piece of software needs to go through software quality assurance testing. It usually involves a human tester creating a series of test cases, and then checking the software interface for bugs and other issues. On Tuesday, MuukTest, a Raleigh, North Carolina startup, announced a new AI agent designed to help build those tests in a much more automated fashion. "Since we started, the vision has been automating software QA technology to reduce test creation to a click, and that has been the whole vision," company co-founder and CEO Ivan Barajas Vargas told TechCrunch. These tools enable testers to test every menu, button and operation in the software user interface under multiple conditions to try and catch as many bugs as possible before the software is released to the public. CTO and co-founder Renan Ugalde says that Barajas Vargas has been involved in software quality assurance testing for 20 years, and they wanted to use Ugalde's engineering prowess to capture that deep understanding and train an AI agent to help build the test suites. They have combined a number of AI technologies to put that knowledge to work including multiple large language models, traditional machine learning, computer vision and image recognition. "We trained AI agents to think just like a QA tester, to understand the context within the application - understand what a menu is, what an input is, and when do you expect to see something," Ugalde said. This requires reinforcement learning, as well as more information about the overall context and all the experience that the two founders have in QA to translate that into an agent. Over time, 'AI agent' has emerged as a term for AI-fueled software that assists with a task or set of tasks, but to this point there is no standard definition. For MuukTest, it acts as an intelligent assistant, performing some of the more mundane tasks that a human QA tester would have traditionally done. The founders immigrated to the U.S. from Mexico in 2011. Barajas Vargas eventually settled in at Dell, while Ugalde landed at IBM, as both grew into their careers with increasing levels of responsibility. They joined forces and launched MuukTest in 2019 where the goal has always been to reduce the amount of effort required to generate and run QA tests. Early versions of their solution used no-code and algorithms to create the tests, but with the new generative AI product, customers can simply describe the kind of test suite they want and MuukTest creates it for them automatically. They can then run the set of tests created by the AI with a single click, greatly reducing the amount of effort required, he said. MuukTest really began to find product-market fit at the beginning of last year. Even before the AI element was added to the product set, the company was doing well with a 15x revenue increase last year over the prior year, and they think they are ready to grow even faster with the new capabilities. The company, which participated in Mass Challenge, a Massachusetts-based startup incubator, the year they launched the startup, has raised a total of $6 million between investments and grants. With 36 employees and 10 contractors, Barajas Vargas says the company intends to remain conservative when it comes to spending. The new AI agent capability is generally available starting today.

TechCrunch

Tue, 6 Aug, 6:22 PM UTC

Outset's New AI Researcher Can Observe People Using Your Products, Interview Them, and Give You Product Feedback

SAN FRANCISCO, Aug. 28, 2024 (GLOBE NEWSWIRE) -- Outset, the first AI-moderated research platform providing the scale and speed of a survey with the depth of a one-to-one interview, announced today a new feature that enables researchers, designers and product managers to, for the first time, leverage AI moderators rather than people to conduct Usability Testing. The research industry is projected to pull in $140B in revenue in 2024. Between 25 and 50% of that is qualitative research. But those numbers don't represent the massive investment in human capital spent conducting qualitative interviews - not only do researchers spend a huge portion of their time moderating qualitative research, but so do product managers, designers, marketers and consultants. Usability Testing represents a large segment of qualitative research. Traditional qualitative interviews have a lower sample size, are very expensive, and notoriously slow to conduct and get results. Teams often spend a month just conducting and analyzing interviews with only 15 people. It's hard to make broad strategic decisions with so few inputs. "Traditionally, usability testing involved researchers, designers and product managers observing customers interacting with a product one on one," said Aaron Cannon, co-founder and CEO of Outset. "And that's a time-consuming process. Outset allows teams to deploy AI-moderated research where AI can conduct hundreds of dynamic, in-depth usability sessions in a matter of hours and then synthesize the results afterwards. This approach helps teams go incredibly deep into the feedback from their users, but do so at a speed and scale that was never before possible." "Usability testing is often a challenge," said Nii Codjoe, Senior Researcher at WeightWatchers. "Testing new products and features with our users is critical to growing our business. But running in-depth, 1-on-1 interviews can be slow and time-consuming -- and automated testing tools don't always give us the depth of insight we need. With Outset, my team and I have dramatically sped up how quickly we can produce generative, strategic insights. Now, we'll get higher-quality insights faster with Outset's new usability testing features. This new solution allows us to get better products and features to market faster." How it works: Researchers and their design and product partners prepare a usability testing guide.Outset trains itself on the guide and prepares a shareable link for research participants, who can come from Outset's own panels or are provided by the client.Research participants (who are usually compensated for their time) enter the video survey experience using the link. An AI moderator provides an overview and sets up screen sharing with the participant, using voice and text prompts.As the participant navigates through the product experience on video, the AI moderator follows their movements and asks real-time contextual questions about their experience. It can listen to voice responses from the participant and ask dynamic follow up questions.The experience can last anywhere from 10 to 60 minutes, and Outset can conduct dozens or even hundreds of sessions at once.Once the sessions are completed, Outset synthesizes feedback and provides topline insights to the team for further review and action. Outset's Usability Testing feature is available now as part of its complete AI-moderated research platform. About Outset Outset is the first AI-moderated research platform providing the scale and speed of a survey with the depth of a one-to-one interview. Designed to help Fortune 500 enterprises obtain deep, qualitative research results quickly, Outset leverages AI to conduct and synthesize video interviews to inform strategic business decisions. Dozens of enterprises, including WeightWatchers, Away and Opendoor rely on Outset to deliver in-depth data results and insights faster than traditional survey methods. Learn more at http://outset.ai. A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/29cd47b8-0134-4a9a-baf4-64930a01e6fc Media Contact: Kerry Metzdorf Big Swing kerry@big-swing.com 978-609-0766 Market News and Data brought to you by Benzinga APIs

Benzinga

Wed, 28 Aug, 2:01 PM UTC

Merlin promises you 20+ AI tools to work with

Merlin is a defines itself as "26-in-one AI assistant to research, create and summarize." The tool merges advanced AI features into a single platform. Yet, the question remains: how well do these features truly work in practice? Merlin is a comprehensive AI-powered assistant designed to enhance productivity by integrating advanced natural language processing (NLP) models like GPT-4 and Claude-3 into everyday tasks. It uses machine learning algorithms to understand and generate human-like responses, enabling users to interact with websites, documents, and applications through contextual conversations. Merlin AI is a versatile tool designed to enhance productivity by integrating with your browser. Whether you're using it on Chrome, iOS, or Android, here's a quick guide to get started, followed by our experiences with some of its features. Sign up and install: 10 best AI tools for business: Top AI business solutions Merlin's capabilities include text summarization, email writing, code interpretation, and content creation, all facilitated by a cloud-based infrastructure that ensures real-time data processing and interaction across various platforms. Through its multi-model support, Merlin AI provides versatile solutions for research, development, and creative tasks within a unified interface. Merlin allows users to interact with AI across all websites, enhancing productivity by answering questions or generating content directly within the browser. Users can access over 20 advanced AI models for varied and dynamic interactions, including models like GPT-4 and Claude-3, providing diverse responses for different needs. Merlin integrates with Google search, providing instant AI-generated answers for faster, more relevant search results. It offers the ability to interact with and query any website directly through AI, making browsing more interactive and informative. Users can upload documents and chat with them, asking questions or requesting summaries, which enhances document review and comprehension. Stay updated with the most recent information from web searches, as Merlin pulls and interacts with fresh data for you. Merlin assists in drafting and responding to emails on Gmail, improving efficiency in communication. A built-in code interpreter helps you write, understand, and troubleshoot code, useful for developers and learners alike. This feature generates summaries of YouTube videos, saving time for users who want a quick overview of video content. Merlin can summarize blogs, providing concise overviews of lengthy articles for quicker consumption of information. It helps craft tailored responses for LinkedIn direct messages, improving professional communication. Enhance LinkedIn networking by leveraging AI to assist in making connections and optimizing profile engagement. When shared, Merlin can summarize the content of Google links, offering quick previews of web pages. Users can get detailed explanations of coding syntax or segments, making complex code easier to understand. Merlin transcribes YouTube videos, creating readable text versions of the video content. This feature allows users to generate AI chatbots based on a person's public tweets, adding a fun or educational dimension to social media engagement. Merlin can generate custom AI-based images or artwork, ideal for creative projects or visual content creation. Convert YouTube video highlights into tweetable content, making it easier to share key insights on social media. Interact with various custom-made chatbots that the community has created, opening up new possibilities for personalized AI interaction. Users can save and access frequently used prompts in a personal library for easier and faster access in the future. Merlin can help craft comments for Twitter, engaging in conversations and responding more effectively across the platform. All screenshots used above are shared by Merlin. Of course, for those making decisions, testing out Merlin AI thoroughly is a must. It's a process that involves exploring each feature in depth to determine how well it integrates into your workflow. While we've put it through its paces, results can vary based on individual needs and expectations. It's a long road to assess its full potential, so perhaps the best approach is to try it yourself and see if it aligns with your requirements. You might find some features more useful than others, or discover limitations we didn't encounter.

Dataconomy

Mon, 23 Sept, 10:06 AM UTC

How to Test AI Prompts to Ensure They Deliver Superior Results

Testing AI prompts is crucial to ensure they meet your specific requirements and deliver optimal performance. This guide by Mark Kashef presents practical methods for evaluating prompts using various tools and platforms, providing actionable steps suitable for both beginners and experienced prompt engineers. By thoroughly testing your prompts, you can refine them to achieve better results and create AI systems that are more responsive, accurate, and effective in real-world applications. Fine-tuning a GPT model is a powerful technique for testing prompts in conversational settings. By adjusting the model's parameters, you can generate back-and-forth exchanges that closely mimic real-world interactions. This approach allows you to assess how well your prompts perform in various scenarios and identify areas for improvement. Fine-tuning enables you to create a more tailored conversational experience, ensuring that the AI can handle diverse inputs effectively. Key benefits of using custom GPT for conversation simulation include: Here are a selection of other articles from our extensive library of content you may find of interest on the subject of writing prompts for artificial intelligence and AI tools : The "GPT for Sheets" add-on provides a user-friendly way to test prompts directly within Google Sheets. This integration eliminates the need for coding, allowing you to create and execute prompts using the familiar spreadsheet interface. By using Google Sheets, you can quickly iterate on your prompts and see immediate results, making it an ideal solution for those who prefer a visual and straightforward approach to prompt testing. The GPT for Sheets add-on offers several key advantages: Combining Airtable's flexible database capabilities with Make's automation features provides a robust solution for managing and testing static prompts. By setting up scenarios in Make, you can systematically evaluate prompt performance across multiple language models, ensuring consistency and reliability. Airtable's intuitive interface makes it easy to organize and track testing results, allowing you to identify patterns and make data-driven decisions. The benefits of using Airtable and Make for static prompt testing include: For testing prompts in dynamic, back-and-forth conversations, Airtable and Make offer a powerful solution. By creating scenarios that mimic real-world user interactions, you can evaluate how your prompts perform in various conversational contexts. Automating these interactions allows you to efficiently identify strengths and weaknesses, leading to more refined and effective conversational AI. Key advantages of using Airtable and Make for conversational prompt testing include: For a comprehensive approach to prompt testing, a custom-built tool integrating JavaScript, Python, and various AI models offers unparalleled capabilities. Such an advanced tool enables visualization of conversations, grading of interactions, and generation of detailed PDF reports. By using these features, you can gain deeper insights into prompt performance, facilitating precise adjustments and improvements. Visualizing and grading interactions helps in understanding the nuances of AI responses, leading to better optimization. The benefits of using a custom-built tool for prompt testing include: Regular testing and refinement are essential for maintaining the quality and relevance of your AI prompts. By identifying potential issues early, you can make necessary adjustments to improve performance and user experience. The tools and techniques outlined in this guide, such as Google Sheets, Airtable, Make, and custom-built solutions, provide efficient ways to manage and test prompts. These methods are valuable for enhancing customer interactions, automating tasks, and generating high-quality content across various industries and applications. Thorough prompt testing is a critical step in developing effective and reliable AI systems. By investing time and resources in evaluating and optimizing your prompts, you can unlock the full potential of AI technology and deliver exceptional results. Whether you are an entrepreneur, developer, or organization looking to enhance your AI workflows, the strategies presented in this guide offer a solid foundation for achieving optimal performance and driving success in your AI initiatives.

Geeky Gadgets

Sun, 29 Sept, 8:00 AM UTC

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