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Why normalized data is critical for best-in-class retrieval-augmented generation (RAG)
Customer data is essential to supporting most retrieval-augmented generation (RAG) use cases.
It allows SaaS products to power enterprise AI search, provide targeted lead recommendations, surface high-fit candidates, and more.
But customer data, in and of itself, isn’t enough to power your product’s RAG use case(s) effectively. This data also needs to be normalized, or standardized and transformed into a consistent format across multiple systems or platforms.
You can read on to learn why normalized data is pivotal for RAG.
Generate more accurate outputs
When customer data is normalized, the data is presented in a consistent format and only includes the necessary information. As a result, the embedding algorithm can easily understand the semantic similarities (and differences) between different types of data.
All of the data that’s semantically similar has embeddings (i.e., vectors) that are close to one another in the vector database, while non-normalized data can lead to embeddings that are more spread out in the database.
For example, say all of your employee objects and fields are normalized, including the “Manager” field. Here’s how this normalized data can support the retrieval portion of your RAG pipeline:
1. A user makes a query in, say, your enterprise AI search, like “Who reports to John?”
2. The embedding algorithm would then transform the query’s plain text into a vector, or a numerical representation of the text’s meaning.
3. The embedded query would be compared to embeddings in the vector space, and all of the embeddings that are close to the embedded query (i.e., all of the employees who report to John) would be fetched.
4. Both the embeddings that are fetched and the embedded query get fed to the LLM you use.
5. The LLM can then generate an accurate response (e.g., “Michelle, Michael, and Sammy report to John”).
https://www.merge.dev/blog/ai-connector?blog-related=image
Prevent sensitive data from being retrieved
The process of normalizing data also gives you the opportunity to remove certain types of data. This gives you more control over the information that’s retrieved and used by the LLM.
For example, say you’re normalizing customers’ employee data but you don’t want the LLM you use to retrieve their personally identifiable information (PII).
You can normalize the employee data such that all PII fields, like social security numbers, are removed before the data gets embedded and added to the vector database.
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Avoid duplicate data in outputs
Similar to the benefit above, the normalization process allows you to deduplicate data.
So, for example, if there are 5 duplicate employee records in a customer’s HRIS, normalizing them could lead 4 to get removed.
This allows the LLM to retrieve just a single copy of the record and, in turn, only generate a single copy in its outputs.
https://www.merge.dev/blog/rag-benefits?blog-related=image
Leverage normalized data across your RAG use cases by using Merge
Merge, the leading unified API solution, lets you add hundreds of integrations through a single build.
Merge also normalizes the data that’s synced from all these integrations according to its Common Models, or predefined data models. This allows you to access normalized data from all of your customers’ ticketing systems, HRISs, ATSs, CRMs, file storage solutions, and accounting systems.
Merge also has advanced features to give you full control over the customer data you can and can’t sync and normalize, such as Scopes.
Learn more about how Merge can normalize your data and discover how companies use Merge to power their RAG use cases by scheduling a demo with one of our integration experts.
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