Retrieval Augmented Generation briefly defined
Retrieval-Augmented Generation (RAG) is a generative AI approach that combines the capabilities of large language models (LLMs) with specific knowledge sources. Text generation is supplemented by a system that retrieves relevant information from databases, documents or other sources in real time. This makes it possible, for example, to chat with documents and generate answers that are not only contextually relevant but also based on current and specific data.
The advantages
- RAG demonstrably reduces hallucinations, as answers are only given from the available content.
- RAG always provides up-to-date content, whereas trained models only contain knowledge up to a fixed deadline.
- RAG is more cost-effective than fine-tuning your own model, including the continuous adjustments required.
The implementation seems easy
With today's libraries and tools, setting up a RAG solution may seem straightforward for companies with the necessary resources. In just a few weeks, one or two developers can set up a GPT interface, connect OpenAI or another model and upload a few documents. The result often works surprisingly well and initially generates enthusiasm within the company. Due to this manageable effort, the solution initially appears cost-effective: users can simply upload a document and interact with it.
Just the tip of the iceberg - the fast start is not the end of the journey
However, if higher demands are placed on document volumes and the connection of additional data sources in terms of result quality and data governance, things become more difficult. Many companies underestimate the effort involved in scaling, security aspects and the high quality of a chat application. While the first 80 percent of the functionality is quickly achieved, the remaining 20 percent requires a disproportionate amount of resources - the so-called Pareto principle comes into play here.
Challenges of a self-built RAG solution
- Access rights and security
A self-built RAG system initially lacks a sophisticated authorization structure. What is standard in corporate IT - for example, that only authorized employees may access certain documents - is often not considered. Companies are then faced with the question: How do I guarantee the security of my data? - Connection and synchronization of the systems
In addition to managing access rights, the biggest challenge is connecting the data from systems such as Confluence, Microsoft SharePoint etc. to the AI and continuously synchronizing it, to keep it up to date. - Processing of documents
Handling the documents so that the generative AI recognizes the necessary passages for correct answers is also anything but trivial. How do the documents have to be broken down? How do you deal with images, tables, etc.? Not every IT department wants to or can deal with this. - Compliance and Governance
Which documents can legally be uploaded into the AI system? Which regulatory requirements apply? Companies face the challenge of adhering to data protection and compliance requirements that are often not considered in a quick self-build. - Quality of results
Most RAG systems use embeddings, i.e. a vector search, to capture the meaning and context of data. Other methods with high-quality linguistic capabilities and the consideration of (technical) thesauri (generic and subordinate terms, synonyms, etc.) as well as Named Entity Recognition - NER (recognition of persons, places and other entities) must first be supplemented. They significantly improve the extraction of important information and metadata. - Continuous further development
AI technologies are developing rapidly. New models, better search technologies and more efficient embedding processes are constantly appearing. Keeping your own solution up to date requires constant development effort. - Usability and integration
A prototype can be built quickly, but a fully integrated company solution that harmonizes with existing systems and is accessible to all employees requires far more effort than initially thought.
Questions companies should ask themselves
Before IT departments decide to build their own RAG solution or purchase an AI assistant, they should ask themselves the following questions to ensure that the chosen solution will meet current and future requirements.
- What is my use case and the goal?
- How many documents should be considered for answers or summaries?
- Which data sources do I want to connect to answer questions?
- Will it stay with these data sources, or should more be added later?
- What about access and read rights? Who can access which information? Is there any sensitive information?
- Can the solution deliver results that satisfy all company stakeholders, including users, management, and the works council?
- Is this result in an appropriate cost/benefit ratio?
Why “buy” may be the better solution
Professional AI assistant solutions also use RAG but come with additional standard capabilities.
- Easy integration: Our iFinder solution with iAssistant can be seamlessly integrated into existing IT landscapes such as SharePoint, Confluence or other DMS without having to duplicate data.
- Secure access controls: Company-specific access rights are automatically considered so that only authorized users have access to relevant data.
- Compliance-ready: Our solution takes legal and regulatory requirements into account and offers mechanisms for GDPR compliance
- Optimized results: By using proven sophisticated machine learning and linguistic techniques as well as continuous model improvement, we deliver more accurate answers to complex queries.
- Future-proof: We continuously update our technologies and use the latest developments in generative AI.
Conclusion: weighing up the costs/benefits
While quickly building a RAG solution may seem attractive in the short term, complex applications, higher quality standards, scalability and security requirements demand ongoing investment. A professional standard system such as iAssistant provides a powerful and future-proof solution for companies looking to leverage generative AI efficiently and securely. Experienced service teams also support AI projects.
