If you are exploring alternatives to RAG AI tools such as Copilot from Microsoft or NotebookLM from Google, you are at the right place. But before we dive into Augmend, let us first understand the RAG technology starting with LLMs.
LLM limitations that you should be aware of
Base LLM models are trained on vast publicly available digital information but have some serious drawbacks.
- • Since LLM training is a resource intensive operation, continuous training is impossible which means there is a cut-off date for training data. Data newer than the cut-off data is not available for training the model
- • LLMs are trained on publicly available data, so they do not have an understanding of proprietary knowledge within companies
- • LLMs are black box models so the information that they generate essentially cannot be traced back to any sources from which it is generated
- • LLMs hallucinate, i.e., confidently generate answers that are believable but not necessarily true. They just generate the next probable word (or better said token) based on the preceding sequence of words without taking into account if that is the truth or not.
What is Retrieval Augmented Generation or RAG? and how does it work?
RAG is a very powerful technique that massively expands the utility of LLM models by overcoming the limitations mentioned above. A RAG process essentially has three parts – retrieval, augmentation and generation:
- • In the first Retrieval step, a Search is conducted to filter out the most relevant documents that may contain answers to the questions asked by the user. Please keep this in mind, as this step has its own limitations that will become clear later.
- • In the Augmentation step, the prompt and question by user is augmented by the retrieved information in order to feed to the LLM model. This makes it possible to quote sources from which raw information was accessed
- • In the Generation step, the LLM generates a tailored response that is grounded in the latest proprietary or public information used
Where can you use RAG?
This makes a whole bunch of use cases possible. To name a few:
- • AI based enterprise search system that taps into company documents and retrieves documents accurately or even better delivers summarized answers
- • AI chatbots for customer support. Such chatbots can tap into the latest company documentation and problem history to give accurate answers
- • Document summarization for meeting transcripts, contracts, articles, etc. to efficiently communicate findings, conclusions and actions.
Can RAG be used for quantitative analysis of your data?
The primary purpose of RAG is to retrieve relevant documents or accurate information, and not to do quantitative analysis. Consider a simple example from the procurement domain – let us say you have 1000 contracts in your database and you ask your RAG system to list all high-risk contracts that are set to expire in the next 6 months. You will most likely get a list of a few top companies where the risk is high but it will not produce an exhaustive list. This limitation is inherent to the RAG approach: note the first step in RAG is Search and in this step the RAG system will identify a few top documents that are most relevant, and it will only be able to generate results from those documents. The purpose of a RAG system is to give contextually rich and fast response, not to be exhaustive.
Augmend's advantages vis-a-vis RAG
Augmend is completely different from RAG although both are based on LLM technology. With Augmend, you structure the information contained in the 1000 contracts once and for all. Of course, as new contracts are made, you can also extract information from them and add them to your structured database. Augmend creates this structured "database". Now your question on listing all high-risk contracts that are set to expire in the next 6 months is quite easy to answer. And the core idea behind Augmend is not to just structure information to answer one question but many such important questions. So, you essentially lay a foundation of clean structured data that gives you highly accurate answers.
But the scope of Augmend is even broader than that. Now take a step back and look at how even structured data enters into your enterprise systems, let us say customer details in your CRM or master data on your raw materials. You might be receiving this information from outside parties in an unstructured format, as emails or pdfs. Some of this data is being manually entered by your employees into the relevant systems such as Oracle, SalesForce, SAP (unless all your data comes in through EDI or digital forms). This is very laborious and tedious! Moreover this process is prone to errors and inconsistencies, and there is never enough resources to enter all information that you get Augmend can help you with this challenge too, because this challenge is at its core the same as the previous one: You need accurate and complete information from unstructured data.
The table below shows side by side the key differences between Augmend and the Retrieval Augmented Generation techniques.
| Feature / Capability | Augmend | Retrieval-Augmented Generation (RAG) |
|---|---|---|
| Core Function | Structured data extraction and transformation | Contextual response generation using retrieved documents |
| Data Handling | Converts unstructured data into structured, queryable formats | Retrieves top relevant documents at runtime |
| Accuracy & Exhaustiveness | High accuracy with exhaustive results from structured data | Limited to top retrieved documents; not exhaustive |
| Source Traceability | Full traceability via structured records | Partial traceability via document citations |
| Use Cases | Data entry into databases, data validation, standardization and enrichment | Chatbots, enterprise search, document summarization |
| Scalability | Scales across multiple use cases once data is structured | Scales with retrieval quality; limited by search relevance |
| Quantitative Analysis | Enables precise filtering and analytics across large datasets | Not designed for deep quantitative analysis |
| LLM Dependency | Uses LLMs for extraction, but relies on structured outputs | Heavily dependent on LLMs for generation |
| Hallucination Risk | Minimal, due to structured data foundation | Moderate to high, due to generative nature of LLMs |
| Integration Potential | Easily integrates with enterprise systems (SAP, Oracle, Salesforce) | Typically used as standalone or embedded in chat interfaces |
| Ideal For | Enterprises needing clean, structured, reusable data | Fast contextual answers from document corpora |
Curious how Augmend can transform your enterprise data workflows?
Click the button in the navigation bar to schedule a demo and explore how structured AI can outperform traditional RAG-based systems. For a detailed comparison with traditional OCR approaches, see our comprehensive analysis.