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Conventional RAG Falls Short with Enterprise Databases

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5 Top Use Cases For Driving Business With Data Hub Architecture 

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Conventional RAG Falls Short with Enterprise Databases

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

5 Top Use Cases For Driving Business With Data Hub Architecture 

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Ensure GenAI compliance and governance 

[Read the Whitepaper![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/effective-ai-governance-wp)

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      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
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Monkey See, AI Do - All about CUA

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Taking the AI leap from RAG to TAG

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GigaSpaces, IBM & AWS make AI safer

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      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
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# What is NLQ, Natural Language Query? 

[More Glossary Terms](https://www.gigaspaces.com/data-terms)

Natural language query (NLQ) is an AI analytics capability that allows users to interact with and analyze complex databases using layman’s terms. 

There is no need for a query language like SQL, advanced analytics skills, or even code. Users can type (or, in some advanced cases, even speak) their query in everyday language, and NLQ will perform a series of text-to-SQL functions to retrieve the desired data. 

## How Natural Language Querying Works in Modern Databases 

This is how NLQ turns simple language into structured queries that deliver database-driven insights: 

**Step 1: First, the user poses a question in plain language.** Say, “How did our XYZ sales figures compare to last month’s?” 

**Step 2: Next, the NLQ interprets the intent of the question** by: 

  * Parsing the query into its grammatical parts 


  * Identifying word meaning and context 


  * Finding core entities like names, places, and dates 


  * Mapping the interpreted findings to SQL functions and database structures 



**Step 3: Third, the NLQ creates a SQL query** from the intent of the natural language query (derived in Step 2). 

**Step 4: The NLQ executes the SQL query and presents the findings** in human-readable form. This could be a graph, a chart, or natural language. Some even generate narratives and provide additional context. 

NLQ turns complex data retrieval and analysis into a conversational exchange. 

## Key Benefits of Using NLQ in Enterprise Environments 

Businesses are just beginning to explore the benefits of utilizing natural language queries in their enterprise environments. Here are just a few: 

  * **The democratization of information:** Once, only highly trained [data analysts](https://www.gigaspaces.com/data-terms/data-analytics) who knew SQL or other query languages could plumb databases for key information. That information would then have to be interpreted and presented to key stakeholders, risking dilution or loss at every step. Now, anybody can ask anything about a complex database and, using NLQ, can get an answer they understand. 


  * **No more lag time for business-critical data** : Data drives decisions. When a large amount of processing must take place before that data is found, much less usable, key decisions lag and opportunities are lost. Using NLQ, businesses are given the ability to act immediately on market trends, make real-time connections they could not make before, and gain greater understanding of complex business data that can provide needed context. 


  * **Optimization of business-generated intelligence** : By virtue of operating in a digital environment, businesses are generating useful and usable information daily, even by the second. Without the means to interpret this data, many companies (especially smaller, more resource-strapped ones) can let that valuable information slip through the cracks. Regardless of whether they have the technical teams to run complex queries, organizations of all maturity levels can leverage NLQ to derive key insights from data they already have, —and that has likely been underutilized until now. 



## Common Use Cases for Natural Language Query Interfaces 

Being able to search intricate technical databases with simple layman’s terms opens up a wide range of opportunities, expanding the speed and ease with which humans can get answers. Common real-world use cases for NLQs include: 

  * **Customer service** : NLQ-powered chatbots and virtual agents can decipher the meaning of typed questions and produce accurate, relevant responses, even walking users through troubleshooting procedures. 


  * **Real-time business decision making:** Every decision maker at an enterprise can have equal access to relevant insights and hard-hitting data. Without a technical barrier to statistics, employees of all backgrounds can quickly leverage business-critical information (“How much did we make in sales last quarter? What are our top-performing assets?”) to make better-informed decisions. 


  * **Search engines** : Today, even search engines like Google and Bing use natural language querying to refine user searches, delivering top results based on context as well as keywords. 


  * **Financial analysis:** The financial industry is employing NLQ as a handy alternative to traditional macros. This empowers financial professionals to fully utilize the data at their disposal to make comparisons, spot trends, visualize financial artifacts, and make predictions, —all without needing to learn a complex querying language. 


  * **Smart systems:** Voice-controlled devices and virtual assistants constantly leverage NLQ tools to translate everyday commands (“Alexa, turn on the TV”) into SQL code that carries out the necessary technical implications. 



## **FAQs**

### How does natural language querying differ from traditional query languages like SQL? 

NLQ (natural language querying) differs from traditional query languages like SQL in that it accepts human speech as an acceptable input, —no coding required. It then translates that language into SQL commands which then produce the desired data. 

### What types of databases support NLQ functionality? 

The following databases support NLQ functionality: 

  * **Analytical databases:** For storing business intelligence. 


  * **Relational databases:** For storing data points that are related to one another (customers and their email addresses, for example). 


  * **NoSQL databases:** For speed, scalability, and performance; they accommodate large amounts of data and heavy use loads. 


  * **Vector databases** : For handling multi-dimensional data, or “[vector embeddings](https://www.gigaspaces.com/data-terms/vector-embeddings)” that are vital for translating unstructured data into a machine-usable format for AI applications. 



### Can NLQ tools be used by non-technical users in business teams? 

Yes. NLQ tools are specially designed to only require everyday language as input, not code or a querying language like SQL. This makes them ideal for use by non-technical business users, in particular. 

### How accurate are natural language queries in retrieving structured data? 

The capabilities of natural laguage queries are still evolving, but at present, they are highly successful at retrieving structured data given simpler queries and well-defined domains. Amazon tests have demonstrated[ 95% accuracy](https://aws.amazon.com/blogs/machine-learning/enterprise-grade-natural-language-to-sql-generation-using-llms-balancing-accuracy-latency-and-scale/) over 100 NLQ cases. Improved[ prompt engineering](https://www.gigaspaces.com/question/what-role-does-prompt-engineering-play-in-generative-ai) will also increase the accuracy of results. 

### What are the most common challenges with implementing NLQ? 

When implementing NLQ in existing workflows, common challenges include understanding language nuances like sarcasm, interpreting complex demands, and establishing sufficient data governance policies. In cases of ambiguity,[ hypothetical document embeddings (HyDe)](https://www.gigaspaces.com/data-terms/hypothetical-document-embeddings) can improve NLQ results. In cases of complexity,[ chain of thought (CoT) prompting](https://www.gigaspaces.com/blog/chain-of-thought-prompting-and-explainable-ai) can help systems avoid confusion, with NLQ acting as the input and CoT as the internal reasoning process. 

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