• Written by Azhar Aliyar, Director

A category manager comparing two suppliers in the procurement database may wonder what is retrieval augmented generation, a technique used by AsscherAi to answer questions about an organisation's own data. Retrieval augmented generation is a method of generating answers by retrieving relevant information from a company's existing data stores, such as the purchase order table or inventory management system, rather than relying solely on the model's memory. This approach allows for more accurate and up-to-date responses, as it is based on the organisation's own data rather than pre-trained models. The category manager can use AsscherAi to ask questions about the suppliers, such as their delivery schedules or product quality, and receive answers based on the data stored in the procurement database.

Definition and explanation

Retrieval augmented generation involves retrieving relevant data from a company's databases and using this data to generate answers to user queries. This is different from traditional language models, which rely on pre-trained models and may not have access to the organisation's specific data. The retrieval process is typically done using a search algorithm, which identifies the most relevant data based on the user's query. The category manager can use this feature to compare the delivery schedules of different suppliers and make informed decisions.

The use of retrieval augmented generation allows AsscherAi to provide more accurate and relevant answers, as it is based on the organisation's own data. This approach also enables the model to learn from the organisation's data and improve its responses over time. For example, if the category manager asks about the product quality of a specific supplier, AsscherAi can retrieve the relevant data from the quality control database and provide an answer based on that data.

Difference from language models

Retrieval augmented generation is often confused with traditional language models, which rely on pre-trained models and may not have access to the organisation's specific data. However, retrieval augmented generation is a distinct approach that uses the organisation's own data to generate answers. This approach is particularly useful for large organisations with complex data systems, as it allows for more accurate and up-to-date responses. Most people get wrong that retrieval augmented generation is just a type of language model, but it is a separate technique that uses retrieval to generate answers.

The main difference between retrieval augmented generation and traditional language models is the source of the data used to generate answers. Traditional language models rely on pre-trained models, while retrieval augmented generation uses the organisation's own data. This difference is crucial, as it allows retrieval augmented generation to provide more accurate and relevant answers. For instance, if the category manager asks about the current stock levels of a specific product, AsscherAi can retrieve the relevant data from the inventory management system and provide an answer based on that data.

Applicability in a large organisation

Retrieval augmented generation is particularly useful in large organisations with complex data systems. For example, a shift supervisor can use AsscherAi to ask about the maintenance schedule of a specific machine, and receive an answer based on the data stored in the maintenance log. This approach allows for more efficient and effective use of data, as it provides a single interface for accessing and querying different data sources. The shift supervisor can also use AsscherAi to compare the maintenance schedules of different machines and identify potential issues.

The use of retrieval augmented generation in a large organisation can also help to improve data consistency and reduce errors. By providing a single interface for accessing and querying different data sources, AsscherAi can help to ensure that data is accurate and up-to-date. For instance, if the shift supervisor asks about the current production levels of a specific product, AsscherAi can retrieve the relevant data from the production database and provide an answer based on that data. If you want to know more about how AsscherAi can be used in your organisation, you can visit our website at AsscherAi to learn more.

Limitations and potential drawbacks

While retrieval augmented generation is a powerful technique for answering questions about an organisation's own data, it is not a panacea. One potential limitation is that it requires a well-organised and easily searchable data system. If the data is not well-organised or is difficult to search, the retrieval process may not be effective. Additionally, retrieval augmented generation may not be suitable for questions that require complex reasoning or inference. For example, if the category manager asks about the potential impact of a change in supply chain on the organisation's overall profitability, AsscherAi may not be able to provide a complete answer.

Another potential drawback is that retrieval augmented generation may require significant computational resources, particularly if the organisation has a large and complex data system. This can make it difficult to implement and maintain, particularly for smaller organisations. However, for large organisations with complex data systems, the benefits of retrieval augmented generation can outweigh the costs. If you have any questions about how AsscherAi can be used in your organisation, you can contact us at AsscherAi contact us to learn more.

When it is the wrong choice

Retrieval augmented generation is not suitable for all types of questions or applications. For example, if the question requires complex reasoning or inference, or if the data is not well-organised or easily searchable, retrieval augmented generation may not be the best approach. In these cases, traditional language models or other techniques may be more effective. It is also important to note that retrieval augmented generation is not a replacement for human judgment or expertise, but rather a tool to support and augment human decision-making. By understanding the limitations and potential drawbacks of retrieval augmented generation, organisations can use it more effectively and make better decisions.