Searching complex laws for accurate information is challenging for everyone, from citizens to legal experts. 😕
Eur Lex provides a new search method for laws from the European Parliament, the EU's legislative body. It serves diverse users (MEPs, citizens, journalists, etc.) with its vast library of European regulations and all editions of the Official Journal of the European Union since December 1952!
Most visitors to the European Parliament's website are task-oriented, seeking specific information rather than exploring the institution. Once they find what they need, they typically leave the site.
The challenge lies in creating a search experience that delivers precise results swiftly, while also providing essential context for informed decision-making. The current keyword search through thousand of documents is inadequate for this purpose, leading to significant user frustration 🤯.
Most visitors to the current European Parliament's search page are primarily seeking specific information rather than exploring the institution. The current search function is hindering this goal. Slow, complex searches with limited context and buried options create a poor user experience. To better serve users and encourage deeper engagement, the website needs to prioritise discoverability.
This project aims to enhance information accessibility across multiple websites by improving search functionality. It also aims to create another way into this library by having a standalone application that can feed into our compliance solution. By doing so, we want to help both internal and external stakeholders find information more quickly.
We want to:
My role as UX designer was to facilitate discovery sessions with the Product and Engineering teams and translate this feedback into visual concepts and user flows.
The EU Hemicycle is the chamber where MEPs debate and vote on European laws.
Our team was given access to the European Parliament's analytics and feedback from the site.
We worked with members of the European Parliament to work on task analysis using analytic tools. This identified the primary tasks performed by both internal and external users on the website. The analysis also assessed the platform’s effectiveness in guiding users to relevant content and documents.

Analysis showed that the overwhelming majority of website traffic comes from search engines, split evenly between mobile (52%) and desktop. Users primarily seek news, facts, or reference materials. This confirms information consumption, not engagement.
Top search terms
As you can see it is a varied mix of content.
The product team conducted an event storming workshop to map the entire user journey for the search flow. This collaborative and visual approach, using a Miro board and sticky notes, resulted in a comprehensive blueprint that informed our wireframing process and encouraged creative problem-solving.
Current task flows mapped out
Issues and painpoints found and proposed solutions by our team
Although temperature and top_p are often considered technical parameters primarily relevant to engineers, they hold big implications for designers. An understanding of these parameters is crucial for creating AI driven experiences that align with user expectations and preferences.
Temperature is the measure of entropy or chaos in generated output. It is a setting that controls randomness when picking words during text creation. Low values of temperature make the text more predictable and consistent, while high values let more freedom and creativity into the mix, but can also make things less consistent.

Top_p or nucleus sampling is a setting that decides how many possible words to consider. A high top_p value means the model looks at more possible words, even the less likely ones, which makes the generated text more diverse.

For our purposes we chose a low temperature + low top_p. We want simple predictable results that are targeted at a wide range of users.
Since we are building with AI for an AI system it's reasonable to expect that there are principles to guide us and also v0. I established a set of guiding principles to ensure responsible design throughout our process and I fed these principles into v0.
Use a human-centred approach: Determine if AI adds value. AI should improve the user experience or solve real problems in the compliance domain, such as automating repetitive tasks, identifying potential risks, or providing insights from complex regulations.
Use multiple outputs: Recognise the inherent variability of generative AI: When a user inputs product specifications, the AI might generate different compliance strategies over time as regulations evolve or new information becomes available.
Teach effective use: Explain the benefits, not the technology: When explaining our AI, focus primarily on conveying how it makes part of the experience better or delivers new value, vs explaining how the underlying technology works.
Support co-editing of outputs: Give control back to the user when automation fails: This enables users to take over and correct or refine the AI's output.
Calibrate trust with explanations: Explainability and trust are essential in the compliance domain. Users need to understand how the AI arrives at its conclusions and recommendations.
Offer ways to improve outputs: Errors and graceful failure. Acknowledging and handling errors gracefully is crucial for designing robust AI systems.
Recognising different ways of interaction: Provide alternate inputs. Beyond text-based interaction, consider how voice commands, image recognition, or even augmented reality (AR) could enhance the user experience in a compliance context.
AI has changed how we interact with technology. While fundamental design principles remain true, the evolution of AI interfaces demand a new approach to user interaction. Here is a selection of patterns I used.
Sample suggestions guide users by offering potential next steps in a conversation. Presented as a list of 3-5 options, these pre-filled suggestions can be selected to continue the interaction easily.

Provide a specific reference point to guide the AI's response. It's difficult to fully convey our intent in a single prompt, but primary sources offer a rich dataset for the AI to analyse.

For AI tools to be effective, they should produce output that is consistent with your individual style and preferences. Our users expect to see output they are familiar with in their tone and style.

Nudges alert users to actions they can take to use AI, especially if they are just getting started as in-app clues or where they serve users.

We need ways to let it interact with our content directly. Inline actions gives users the ability the let AI adjust parts of a piece of content without regenerating or impacting the whole.

Instead of simply summarising a topic or a primary source, AI can collect information from multiple sources and aggregate it into a single response. Citations help users trace the information contained in a response back to its original material.

I started on a journey of exploration to refine the search interface's user experience. Then experimented with different layouts, information architecture, and interaction patterns to optimise the search flow.
Selecting UX patterns for our flow
Layout ideas
Final flow
Search page wireframe
Results page wireframe
Flow from search to results page
The Eur Lex platform underwent user trials with key user groups, including MEPs, legal professionals, and public users.
The response was overwhelmingly positive, with the redesign achieving its core objectives and delivering a vastly improved, more efficient search experience: