A Beginner’s Guide to Natural Language Generation

guide to natural language generation
Learn how Natural Language Generation works, its key components, how to choose the best NLG-powered tool and how to make the most of it in this short but useful beginner’s guide.

Table of Contents

Introduction to Natural Language Generation (NLG)

Natural Language Generation (NLG) utilizes artificial intelligence algorithms to quickly process and analyze data, extract relevant information, and create written content in various forms, ranging from brief explanations to in-depth articles. 

Tools that are NLG-powered are considered to be efficient because they can generate a large amount of content quickly, and are consistent in style.

How Natural Language Generation (NLG) works

NLG works by analyzing data and then translating the data into human-readable text.

In simple words, NLG-powered tools use special algorithms to look at the data, pick out important information, and write sentences and paragraphs that explain the data.

It works by analyzing data and using algorithms and rules to create a set of languages. This set of language is then used to generate natural-sounding sentences that sound like they were written by a person. 

For example, a computer can generate weather reports from weather data, or generate stock price updates by analyzing stock exchange information. However, the most popular use of NLG is probably for creating content for websites and digital marketing channels.

NLG can also be used to generate content in different languages.

Key components of NLG

There are several components here that work together to generate written content.

It all starts first with data input, where relevant data is collected and organized from various sources.

Then, this data is analyzed by Natural Language Processing (NLP) algorithms, which identify relevant information from the data and extract key insights.

The next component that plays its part is templates and grammar rules. These rules define the structure and format of the output content, such as sentence structure, tone, and style. 

Finally, we have output generation. The NLG software uses processed data, NLP insights, and templates to generate written content. The process is not complete without ongoing evaluation and improvement, which ensures the generated content is accurate, consistent, and relevant.

Applications of NLG in the real world

Natural Language Generation (NLG) technology has numerous real-world applications, improving the efficiency and accuracy of various industries.

For example, in the finance sector, NLG can produce financial reports, summaries, and other written content, freeing up time and resources for human financial analysts.

It can also help businesses analyze customer data and generate personalized marketing content, improving customer engagement and conversion rates.

Customer service can also benefit from NLG as it can automate responses to customer inquiries, freeing up time and resources for human customer service representatives. 

In the field of news and journalism, NLG can generate articles and news reports based on data sources such as financial reports, sports stats, and weather updates.

These are just a few examples of the many real-world applications of NLG technology, demonstrating its ability to produce high-quality written content at scale and transform the way we work and communicate in various industries.

Getting started with Natural Language Generation

Choosing the right NLG-powered tools and platforms

There are many NLG-powered tools available these days. Depending on multiple factors, some less-known tools might actually be a better option for you than those that are widely popular.

So instead of following trends, do your own assessment of tools by asking the following questions:

  • What are the specific use case and the problem you want to solve with NLG?
  • What is the type and volume of data you will be using to generate content approximately?
  • Does the tool allow for the customization of templates, language, and tone to fit your specific brand and content requirements?
  • Is the tool easy to integrate into your existing workflow?
  • Does the tool have a dedicated support team to assist with any technical issues or questions?
  • What’s the cost of the tool, and does it fit into your yearly budget?
  • As your business scales, will the tool be able to scale at the same pace and meet your business needs in the long term?

Answering these questions will help you filter out tools so you use the one that ticks off all of the boxes for you.

Best practices for using NLG

Once you have chosen the best NLG-powered tool for you, it’s time to make the most of it. 

The majority of these tools are like a blank canvas when you first start using them. Depending on how user-friendly their interface is, they can be quite robust and confusing to use.

That’s why it’s important to adapt them to your needs and set up a workflow, or an SOP, that you will follow every time you need to use the tool.

To do that, first, you need to clearly define your goals for using NLG. Then choose data sources that are relevant to your goals and ensure they are reliable, accurate, and up-to-date.

Make sure that you customize the tool’s language to fit your brand and content requirements, so you get consistency in the content generated by NLG.

Remember that thorough testing and iteration are also crucial to optimize your SOP or workflow. So always keep track of results and make tweaks accordingly.

We are currently working on a set of SOPs for different AI writing tools and different use cases. So make sure you subscribe to our newsletter so you can be notified when we launch them!

Let’s sum up

Using NLG-powered tools, businesses can quickly and accurately generate written content from data. 

As we’ve seen, Natural Language Generators can be used for a wide range of applications such as generating financial reports, customer support responses, and marketing content, among others. 

When choosing an NLG-powered tool, it’s important to assess the tool’s features and determine whether it’s capable of meeting your specific needs. Additionally, it’s important to set up a workflow that fits your workflow to optimize your NLG use. 

With the right tool and workflow in place, businesses can enjoy the benefits of NLG, saving time and resources for more important tasks.

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