10 Most Important Interaction Design Principles

The process of interaction design involves studying the behavior and structure of interactive systems and implementing them for developing useful digital products. In other words, interaction design is the relationship between user and product and the services they use.

The purpose of interaction design is to create a great user experience. That’s why most of the UI disciplines require understanding and hands-on experience of interaction design principles. After all, it’s about designing for the entire interconnected system: the device, interface, context, environment, and people. Interaction designers strive to create meaningful relationships between people and the products and services they use. It may include computers, mobile devices, gadgets, appliances, and more.

It is important to understand ux design best practices while developing complex web and mobile applications. These are the key elements that product designers should not neglect while creating an interface for the user.

The 10 most important interaction design principles are-

UX: Match user experience and expectations
Consistent design: Maintain consistency throughout the application
Functionality: Follow functional minimalism
Cognition: Reduce cognitive loads/mental pressure to understand the application
Engagement: Design interactively such that it keeps the user engaged.
User control: Allow the user to control, trust, and explore
Perceivability: Invite interactions through intuitions and interactive media
Learnability: Make user interactions easy to learn and remember
Error handling: Take care to prevent errors, if they occur make sure to detect and recover them.
Affordability: Simulate actions by taking inspiration from usual and physical world interactions.

10 Important Interaction Design Principles

#1 Match user experience and expectations

By matching the sequence of steps, layout of information, and terminology used with the expectation and prior experiences of the users, designers can reduce the friction and discomfort of learning a new system.

You can match your audience’s prior experiences and expectations by using common conventions or UI patterns, for example, Hitee Chatbot.

#2 Consistency

Along with matching people’s expectations through terminology, layout, and interactions, the design should be consistent throughout the process and between related applications.

By maintaining consistency, you are helping users learn more quickly. You can re-apply their prior experiences from one part of an application to another to maintain consistency throughout the design. Design consistency is also an aid to intuitive interfaces.

Bonus – you can use the inconsistencies to indicate to users where things might not work the way they expect. Breaking consistency is similar to knowing when to be unconventional.

#3 Functional minimalism

“Everything should be made as simple as possible, but no simpler.”
Albert Einstein

The range of possible actions should be no more than is absolutely necessary. Providing too many options will detract the primary function and reduce usability by overwhelming the user with choices. To achieve the Zen of functional minimalism, you should-

Avoid unnecessary features and functions
Break complex tasks into manageable sub-tasks
Limit functions rather than the user experience.

#4 Cognitive loads

Cognition refers to the “process of thoughts.” A good user interactive design minimizes the user’s “effort to think” to complete a task. Another way to put this is that a good assistant uses his skills to help the master focus on his skills.

For instance, while designing an interactive interface, we need to understand how much concentration a task requires to complete it. Accordingly, you can design the UI that reduces the cognitive load as much as possible.

Here’s a technique to reduce users’ “thinking work.” Focus on what the computer is good at and build a system that utilizes its abilities to the fullest. Remember, computers are good at-

Maths
Remembering things
Keeping track of things
Comparing things
Spell Checking and spotting/correcting errors

The point is – by knowing the attributes of users and products, one can create a design for a better user experience.

#5 Engagement

In terms of user experience, engagement is the measure of the extent to which the user has a positive experience with your product. An engaging experience is not only enjoyable but also easier and productive. Engagement is subjective to the system. I.e. your design must engage with the desired audience. For instance, what appeals to teenagers might be irrelevant to their grandparents. Apart from aligning your design for the appropriate audience, achieving and creating control is the key.

The interaction design principles state that users should always feel like they’re in control of the experience. They must constantly experience a sense of achievement through positive feedback/results or feel like they’ve created something.

In his book “Flow,” Mihaly Csikszentmihalyi describes a state of optimal experience where people are so engaged in the activity that the rest of the world falls away. Flow is what we’re looking to achieve through engaging interactions. We should allow users to concentrate on their work and not on the user interface. In short, stay out of the way!

#6 Control, trust, and explorability

Good interaction design should incorporate control, trust, and explorability to any system. If users feel in control of the process, they’ll be more comfortable using the system. If the user is comfortable and in control, they’ll trust the system and believe that the application will prevent them from making an unrecoverable error or from feeling stupid. Trust inspires confidence and with confidence, the user is free to explore further. Intuitive interfaces are extremely good at stimulating users to navigate and explore the app.

#7 Perceivability

People are aware of the opportunity to interact with interactive media. As interface designers, we must avoid developing hidden interactions, which decrease the usability, efficiency, and user experiences. In other words, people should not have to guess or look for opportunities to interact.

When developing interactive media, users should have the ability to review an interface and identify where they can interact. We must remember that not everyone experiences and interacts with interface in the same way others do. In the process of interaction design, make it a habit to provide hints and indicators like buttons, icons, textures, textiles, etc. Let the user see that these visual cues can be clicked or tapped with their fingers. Always consider the usability and accessibility of the interactive media and how the user sees and perceives the objects in the interface.

#8 Learnability

Another important interaction design principle is inducing the ability to learn to use the interface easily. In other words, users should be able to learn to use the interface in the first attempt and should not face issues using it again. Please note that engaging interfaces allow users to easily learn and remember the interactions.

Even though simple interfaces may require a certain amount of experience to learn, learnability makes interaction intuitive. People tend to interact with an interface similar to other interfaces. This is the reason why we must understand the process of interaction design thoroughly and the importance of design patterns and consistency.

Intuitive interface design allows users to learn to use the interface without much effort and gives them a sense of achievement. They feel smart and capable of grasping and utilizing newer interfaces. In a nutshell, product designers should let the user feel confident while navigating through the interface.

#9 Error prevention, detection, and recovery

The best way to reduce the number of errors a user makes is to anticipate possible mistakes and prevent them from happening in the first place. If the errors are unavoidable, we need to make them easy to spot and help the user to recover from them quickly and without unnecessary friction.

Error prevention techniques-

Disabling functions that aren’t relevant to the user
Using appropriate controls to constrain inputs (e.g. radio buttons, dropdowns, etc.)
Providing clear instructions and preemptive help
As a last resort, provide clear warning messages.

How to handle application errors through design?

Anticipate possible errors and provide feedback that helps users verify that-

They’ve done what they intended to do.
What they intended to do was correct.

Please note that providing feedback by changing the visuals of the object is more noticeable than a written message.

Error recovery techniques –

If the error is unavoidable, provide direction to the user to recover from it. For example, you can provide “back,” “undo,” or “cancel” buttons.

If a specific action is irreversible, you should flag it “critical” and make the user confirm first to prevent slip-ups. Alternatively, you can create a system that naturally defaults to a less harmful state. For example, closing a document without saving it should be intelligent enough to know the unlikely behavior of the user. It can either auto-save or display a warning.

The spectrum of user interface : interaction design principles

#10 Affordance

Affordance is the quality of an object that allows an individual to perform an action. For example, a standard household light switch appears innately clickable.

The point is – users should get a clue about how to use an abject through its physical appearance. While designing user interfaces, you can achieve affordance either by simulating ‘physical world’ affordances (e.g. buttons or switches) or keeping consistency with web standards and interface design elements (e.g. underlined links or default button styles). The thing is, in an intuitive interface, users are able to navigate and use the functionalities of the application without any formal training.

Interaction design is not always about creating a better interface for the users; it is also about using technology in the way people want. It is necessary to know the target users to design a desirable product for them. Interactive design is the basis for the success of any product. These 10 interaction design principles are based on the study and experiences of our team in designing mobile and web apps for a broad product portfolio and on multiple mobile and web platforms.

Drop us a word at hello@mantralabsglobal.com to learn more about our interaction design projects and services.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled