AI Meets UX: Crafting Experiences That Feel Human

This PDF explores how AI and UX converge to craft interactions that feel human through conversational design. Featuring EnGenie, an AI-driven HR assistant developed with EnFuse Solutions, it demonstrates how intent-based routing, human-like tone, and contextual memory create seamless, empathetic user experiences that bridge technology and human connection.

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1. AI Meets UX: Crafting Experiences That Feel Human
When we set out to build EnGenie, we weren’t just building a chatbot – we were crafting
a conversational experience. One that felt less like talking to a machine and more like
chatting with a helpful HR colleague.

Here’s how we approached conversational design to make AI feel human.
Step 1: Understand The User’s Intent
The first rule of good UX? Know your user.​
We realized early on that not every query needed the same treatment. So we built a
smart routing layer that could:
●​Route policy questions to a RAG pipeline
●​Send holiday queries to a SQL database
●​Handle greetings with a lightweight LLM
●​Manage follow-ups by stitching in past context

2. This intent-based routing made EnGenie feel smarter and more intuitive—because it
responded based on what the user meant, not just what they typed.
Step 2: Speak Like A Human, Not A Bot
Even when the answers were correct, something felt off. The tone was robotic, overly
formal, or just… not helpful.
So we asked: What would an HR rep sound like?
Instead of fine-tuning the model (which is costly and rigid), we used prompt engineering
to shape EnGenie’s voice. Our system prompt included:
●​A friendly, personalized tone
●​Direct address (“You are entitled to…”)
●​Clear rules to avoid hallucinations
●​A fallback message when info wasn’t available
This simple change made a huge difference. Suddenly, EnGenie didn’t just answer – it
connected.
Step 3: Design For Context, Not Just Queries
Follow-up questions like “What about sick leave?” used to confuse the bot. Why?
Because it lacked memory.
We fixed this by:
●​Storing past messages in a conversation DB
●​Merging old and new context before generating a response
Now, EnGenie could handle multi-turn conversations with consistency and clarity just
like a real human would.
Step 4: Make The Invisible Visible
To improve UX, we needed to see what users were experiencing. So we integrated
Langsmith for observability:

3. ●​Tracked query flows (RAG, SQL, etc.)
●​Monitored token usage and latency
●​Logged prompt experiments and outcomes
This helped us debug faster, optimize smarter, and ensure a smooth user experience.
Step 5: Measure What Matters
We didn’t want to rely on gut feel. So we used RAGAS to evaluate:
●​Faithfulness (no hallucinations)
●​Answer relevance
●​Context relevance
●​Context recall
This gave us a clear, data-backed view of how well EnGenie was performing and where
to improve.
Final Thoughts
Building EnGenie showed us that a successful chatbot is more than just AI—it’s a
human-centered experience. From understanding user intent to maintaining context
across conversations, every design choice aimed to make interactions seamless and
intuitive. ​
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By integrating observability and measurement, we ensured that improvements are
data-driven, not guesswork. EnFuse Solutions plays a pivotal role in guiding this journey,
helping businesses create AI experiences that feel natural, reliable, and genuinely
human.
Read more: Building Our Own Chatbot: Why We Decided To Go In-House