What is structured output?
Structured output enables a purchasing manager to query the purchase order table and get a list of suppliers in a format that can be easily consumed by the enterprise resource planning system, allowing for faster and more informed decision making without manual review of large amounts of data.
A purchasing manager at a large organisation is deciding which supplier to use for a new contract, and currently bases it on a manual review of the purchase order table in the enterprise resource planning system. Structured output refers to the process of forcing a model to answer in a schema that a system can consume, such as a database table or a CSV file, allowing the purchasing manager to query the data in a natural language and get answers in real time.
Definition
Structured output is a mechanism that enables a model to produce output in a predefined format, making it easier for systems to consume and integrate the results. This is particularly useful in large organisations where data is scattered across multiple systems and formats. By forcing the model to produce output in a structured format, organisations can reduce the complexity of integrating the results into their existing systems.
The definition of structured output is often misunderstood, but it is simply a way to ensure that the output of a model is in a format that can be easily consumed by other systems. This is not a new concept, but it is particularly relevant in the context of natural language querying, where the output of the model needs to be integrated into existing systems.
Difference from related concepts
Structured output is often confused with data visualisation, but they are not the same thing. Data visualisation is the process of presenting data in a graphical format, whereas structured output is about producing output in a predefined format that can be consumed by systems. While data visualisation can be useful for human consumption, structured output is necessary for system integration.
Most people get wrong the idea that structured output is only about presenting data in a nice format, but it is actually about enabling systems to consume and integrate the results. This is a critical distinction, as it highlights the importance of structured output in enabling organisations to get value from their data.
Applications in a large organisation
In a large organisation, structured output can be applied in various scenarios, such as querying the purchase order table or the customer relationship management system. For example, a shift supervisor can use structured output to query the maintenance log and get a list of pending maintenance tasks in a format that can be easily consumed by the enterprise resource planning system.
The category manager can also use structured output to compare two suppliers and get a list of key differences in a format that can be easily consumed by the procurement system. This enables the category manager to make informed decisions quickly and efficiently, without having to manually review large amounts of data.
By using structured output, organisations can enable their employees to query data in a natural language and get answers in real time, without having to manually review large amounts of data. This can be particularly useful in scenarios where data is scattered across multiple systems and formats, and where manual review is time-consuming and prone to errors.
Limitations
Structured output does not enable organisations to query data that is not already in their systems. For example, if an organisation wants to query data from a external source, such as a website or a social media platform, structured output is not sufficient. In such cases, organisations need to use additional tools and technologies, such as web scraping or APIs, to extract the data and integrate it into their systems.
Organisations can visit our website at www.asscher.ai to learn more about how structured output can be used to enable natural language querying of their data. They can also contact us at contact-us@asscher.ai to discuss their specific use cases and requirements.
When not to use structured output
Structured output is not the right choice when the output of the model needs to be presented to humans in a graphical format. In such cases, data visualisation is a better choice, as it enables organisations to present data in a format that is easy for humans to understand. Additionally, structured output is not necessary when the output of the model is only used for internal consumption, and does not need to be integrated into existing systems.
In general, structured output is a powerful tool that enables organisations to get value from their data, but it is not a silver bullet. Organisations need to carefully evaluate their use cases and requirements, and choose the right tools and technologies to enable their employees to query data in a natural language and get answers in real time.
Frequently asked questions
How does structured output improve decision making
Structured output improves decision making by providing results in a format that can be easily consumed by systems, enabling faster and more informed decisions without manual review of large amounts of data.
What is the difference between structured output and data visualisation
Structured output produces output in a predefined format for system integration, whereas data visualisation presents data in a graphical format for human consumption.
Can structured output be used with external data sources
No, structured output is not sufficient for querying external data sources, such as websites or social media platforms, and additional tools and technologies are needed to extract and integrate the data.
When is structured output not the right choice
Structured output is not the right choice when the output needs to be presented to humans in a graphical format or when the output is only used for internal consumption and does not need to be integrated into existing systems.