The Power of Asking Questions: Optimizing User Search Experience

In a world where instant answers matter, KNOWRON empowers you to ask questions just like you would with a colleague, delivering solutions in seconds.

“Finding instant answers” is our goal. Yet, so that the system can provide an answer, you have to ask a question first. But why should you ask a question as opposed to just doing a keyword search?

At the end of this article, you will have discovered the straightforward effectiveness of formulating proper questions, allowing you to get the exact information you need, to ultimately enhance your productivity.

Precision questions, precise answers: elevating user experience with KNOWRON

Now, imagine yourself communicating with this colleague using keywords: it's not just impractical—it lacks depth!

You can think of Knowron as one of your colleagues, but digital. The more precisely you frame your questions, the more effectively you will find the answer you need.

Think of KNOWRON as more than a tool; it's an ally in your quest for knowledge. The key to unlocking its potential lies in how you ask questions. The more specific and well-framed your queries, the better the answers you receive.

Here’s our insight: searching with full queries trumps simple keyword searches. When you pose well-thought-out questions, you give KNOWRON a deeper understanding of your needs and intentions. This approach empowers the system to delve into its vast resources and fetch not just any answer, but the most fitting one.

It’s all about elevating User Experience to unparalleled levels of efficiency and insight!

Background insights: a general understanding of how Natural Language Search emerged as the favoured paradigm

Traditionally, search systems have required users to express their information needs through keywords rather than natural language questions. However, recent advancements in generative AI, especially since the implementation of the first Transformer Architecture by Vaswani et al. (2017) have reshaped this landscape.

A bit of context:

In 2014, Microsoft Research presented insights into the dynamics of search systems, emphasizing the prevalent use of keywords over natural language queries.

Today,  in contrast to its stance, Microsoft signled an apparent shifted of thinking, particularly by investing 10bln USD in OpenAI.

In case you missed it, OpenAI's Generative Pre-trained Transformer (GPT) distinguishes itself for its proficiency in generating natural language text completions based on given prompts. Their most popular product, ChatGPT, empowered by GPT model, is able to generate context-aware results. Its responsiveness, by which we all value this tool, and overall performance continually improves thanks to users asking questions in natural language.

In less than a decade, what developments prompted Microsoft to shift its perspective in favour of natural language search?

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This timeline outlines the key milestones in natural language search (and NLP) from the emergence of the Transformer architecture to the present day.

Why do we care about users behaviour at search in KNOWRON?

Within this narrative, there is one crucial element: the significance of the questions posed to the assistant.

At KNOWRON we are increasingly observing that using natural language questions instead of keyword-based queries optimises the answer extractions. This shift towards asking questions in a natural, conversational manner aligns perfectly with how NLP-based virtual assistants, like KNOWRON, operate. It's not just about getting answers—it's about getting them instantly. This immediacy is a game-changer, especially in a professional setting where every second counts, making the workforce more effective and responsive.

KNOWRON, as usual virtual assistants, uses natural language processing (NLP) to understand user queries by analyzing the structure and content of the query to determine the user's intent and provide relevant responses.

NLP is a subset of artificial intelligence that enables virtual assistants and chatbots to understand language as humans speak it.

The brilliance of NLP-based virtual assistants, like those at KNOWRON, is amplified when users pose their queries in natural language. Why? Because natural language queries bring a richer context and specificity to the table. This depth enables virtual assistants to better understand the user's intent, leading to more accurate and relevant responses. This is the foundation learning processes, that ultimately yields into better assistants.

What are the differences between keyword-based queries and natural language questions?

1. Structure: Keyword searches are usually brief, using one or more relevant words or phrases. On the flip side, natural language questions are lengthier and more intricate, often including words like who, what, when, where, why, or how.
2. Intent: Keyword-based queries are often used when the user has a specific idea of what they are looking for and want to find it quickly. Natural language questions, on the other hand, are used when the user is looking for more detailed information or has a specific question that they want answered.
3. Search results: Keyword searches can bring back many results, and not all may be relevant. In contrast, natural language questions often yield more precise and relevant results. This is because search engines can better understand the user's intent, providing more accurate answers.
4. Search engine behaviour:  Keyword searches are matched with specific words and phrases on web pages, whereas natural language questions are analyzed for their intent and context. This difference in approach allows search engines to deliver more accurate results when users use natural language queries.

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Examples

Google:

Let's take Google as an example. Imagine you want to find information. First, you can try searching with a few keywords. Then, try asking a specific question about what you're looking for. You might notice that the question gets you a more precise answer right away. This means less time refining your search because natural language questions often give you exactly what you need faster.

> Watch the video: Natural Language Search: Optimized results in Google

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KNOWRON:

Now let’s visualize a common scenario, inside a machinery shop-floor of an electronics manufacturer for instance.

In a complex machine maintenance scenario, navigating through numerous documentation silos can be time-consuming. When faced with an issue, the instinct might be to seek insights from senior colleagues who've encountered similar challenges. However, waiting for their responses could take days, impacting productivity.

This is where a digital assistant becomes invaluable. It acts as an instant, digital colleague you can consult in real-time. The beauty lies in its versatility—you can ask questions using voice commands, just as you would with a human colleague.

And again, you wouldn’t be likely to just throw some words to your colleague to ask your question, right? For instance, instead of throwing keywords like "polylactic acid" or "safety," you can ask, "What safety measures should I take when installing the 3D printer?" This approach ensures precise, direct answers promptly, mimicking the efficiency of a conversation with a knowledgeable colleague.

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In a nutshell, the benefit for your organization:

Asking questions in natural language allows service teams to express their information needs more precisely.

• Enhanced precision: natural language queries boost information articulation, ensuring technicians swiftly access accurate data for efficient task execution.

• Streamlined operations: troubleshooting efficiency increases, minimizing downtime and reducing reliance on external resources.
• Collaborative work environment: seamless query communication fosters teamwork, enabling technicians to share insights effortlessly.
• User-centric interaction: improved internal collaboration results in seamless interactions, elevating customer engagement and satisfaction.

In conclusion, the evolution of NLP-based virtual assistants like KNOWRON marks a significant leap in how we interact with technology. By encouraging natural language queries, we not only enhance the accuracy and relevance of the responses we receive but also elevate our overall experience to one that mirrors human interaction. This advancement is not just about technological innovation; it's about reshaping our approach to information retrieval, making it more intuitive, efficient, and aligned with our natural communication style. The result is a profound impact on our productivity and decision-making processes, especially in professional settings.