Engineering Challenges in Building NLP-Driven UI/UX

NLP-based UI transformation through an engineering lens

Introduction

The rise of large language models (LLMs) and AI has dramatically transformed how users interact with digital interfaces. Traditional search-based and form-driven UI paradigms are rapidly being replaced by conversational, dynamic, and intent-aware interfaces.

As an engineer, I would like to approach NLP-based user interfaces from a technical perspective rather than a UI/UX design standpoint.

This shift is fundamentally altering not only user experience but also backend architectures —the engineering challenges of intent extraction, API interactions, and dynamic data processing. This shift requires rethinking traditional architectures and developing new strategies for handling user input in an intelligent, structured manner.

Prompting…

At a very high level, a prompt can be broken down into the following fundamental components mainly identified in textual prompting (Conversational Assistants, Code Assistants, Dynamic Analytics & Querying, etc)

Example : "Write a haiku about the engineer with 0 subscribers in substack." :D

• Goal: Write a haiku.

• Context: The haiku should be about the engineer with 0 subscribers.

• Constraints: It should be a haiku (5-7-5 syllable structure).

• Input: None (the prompt itself provides the subject).

Knowledge takes new shape,
Empty page now waits to fill,
World will soon discover.

- Poet Gemini

Types of prompting

• Text-to-Text: Traditional text input like ChatGPT.

• Text-to-Image: AI-generated images via DALL·E or Midjourney.

• Image-to-Image: Style transfer, AI-driven enhancements.

• Visual Prompting: Feeding a chart, or image and asking for insights.

Rather than delving into general prompt engineering, I'll focus specifically on its technical implementation and associated challenges.

Technical Shifts: How Intent Extraction is Changing

Traditional intent extraction count on predefined templates, but new approaches dynamically adapt and refine extracted intent. Some of the key transformations include:

1. Dynamic Entity Extraction & Structuring

• Instead of static intent recognition, LLM-based models dynamically identify key fields and pass structured data to APIs.

  Example: Extracting structured fields from "Schedule a meeting with EggPuff Engineer at 3 PM tomorrow" and converting them into an API request:

{
    "action": "schedule_meeting",
    "participant": "EggPuff Engineer",
    "time": "15:00",
    "date": "2025-03-11"
}

Some resources on handling the shift in intent extraction

• Google’s Dialogflow CX – A modern approach to conversational AI that dynamically extracts intents and entities.

• RASA Open Source NLP for Intent Recognition – An open-source library for custom intent classification and API mapping.

2. Context-Aware API Calls

• AI models that map user requests to appropriate API calls dynamically without predefined mappings.

• Example: "Find me a budget-friendly laptop with at least 16GB RAM," and the system translates this into structured API filters.

3. LLM-Based Query Generation for Databases

• Generating optimized SQL queries based on natural language questions.

• Example: "Show me sales trends for the last 6 months" is converted into:

SELECT date, SUM(sales) FROM orders WHERE date >= NOW() - INTERVAL '6 months' GROUP BY date;

Challenges with Conversation UI

While the advancements are interesting, there are few technical challenges must be addressed or already addressed in better ways.

1. Reducing Noise in Prompts

• Many NLP-driven interfaces suffer from noisy, ambiguous inputs. A possible solution is pre-populated templates that guide users toward clearer intent.

  Example: Instead of an open-ended question, providing structured UI hints like “What would you like to do? (Book a meeting, Order a product, etc.)”

2. LLM Routers - Latency & Performance Optimization

• Real-time NLP-based UI must maintain responsiveness while processing large models.

  Strategies:

  • Using hybrid approaches where lightweight models handle initial filtering before routing to more complex LLMs.

  • Implementing edge AI processing to reduce API latency.

Example : https://github.com/lm-sys/RouteLLM

3. Memory Layer - Maintaining Context in Long Interactions

• Many LLM-based interfaces struggle with long conversation coherence.

• Possible solutions:

  • Memory-aware models that retain context across interactions.

  • Stateful API calls that store user session data.

Example : mem0

4. Security & Data Privacy

• Handling sensitive data in conversational interfaces requires strict encryption and anonymization techniques. Masking PII before processing user requests in an LLM system.

Foreseeing the Future: What Could Be Next?

As we move forward, the key question is: How can we make NLP-driven interfaces not just smarter, but also more intuitive, seamless, and truly proactive in understanding user intent?

References

• Paper about Generating Context-Aware API Calls from Natural Language Description using Neural Embeddings and Machine Translation

• Meta’s SeamlessM4T for Multimodal NLP – A new way of processing intent across text, speech, and image input.
  https://ai.facebook.com/blog/seamless-m4t

• Text to SQL paper benchmarks