RAG is an AI in enterprise architectural approach to build AI applications and services. The goal of RAG is to improve what LLM generates. If we are building something for a local government website, we want to help people find information about various things that are going on, like jury duty, or pet licensing. To start with, RAG apps have a pre-processing step, where you need to create the embeddings for all webpages (including forms, brochures, or the images) the data you want to use for grounding your responses. We need to store that data with embeddings, in a vector database. What happens when a user asks a question from the chatbot? The user asks a question, calls a prompt and uses the same embedding model we used in our pre-processing step, to generate an embedding or a value of that user’s question.
Then we are going to use that embedding we just generated, to find some number of related pieces of data, in our data, in a data store using a similarity algorithm. We will see that all the similar data will end up with similar vectors which will make it easy for the vector database to find all the related information. Once we find that related information, we will add it to the original prompt, augmenting what the user gave us.
When we retrieve the related data, are we retrieving the actual vector or the embedding, or we are retrieving the original piece of data that we made the embedding off of? We use the embedding, the big array of numbers to find similar pieces of data, but the vector database returns the original data in the original format (text, photo, video, audio). So we will add the original data that we got into the LLM, and give that to the LLM to generate the answer. So when we have added the relevant data to the prompt, we can send it to LLM like Gemini. When we pass Gemini’s response to the user, its response should contain an answer to the question that the user asked.
The Dataflow for the most basic RAG application
So to summarize this:
- We generated an embedding of what the user’s question was.
- We used that embedding to find relevant data in our vector database.
- We took all that relevant data and added that to the prompt that we sent to the LLM to get a response.
- We sent that response back to the user with the answer.
How does it work?
We quickly discussed how fast it works, and how it directs LLM’s to give accurate responses. When a user asks a question, the RAG system searches a database, a collection of documents, etc. to find relevant data snippets. This is then added to the original user prompt, creating an “augmented” request. This is added to the LLM, to produce a more accurate, and contextually appropriate response.
What is the outcome when this technique is harnessed with a subscription model?
For the most part, RAG as a Service works closely with the Large Language Model, moving up with accurate results in real time. This service handles complex infrastructure, data integration, vectorization, and synchronization, allowing developers to focus on creating applications rather than building RAG systems from scratch.
You can’t skip this in 2026, because
When information is available at a single click at the blink of an eye, it prevents the problem of outdated information. With Chat GPT LLM you can’t really make out if whatever it is offering is new, and most relevant. It will answer the way you ask, without acknowledging the righteousness of the information. It provides a managed solution that enables businesses to use real-time data to power context-aware AI applications.
RaaS platforms offer (1) data encryption, (2) role-based access, and (3) compliance with industry standards.
With RaaS solutions it is easy to integrate existing CRM, internal tools with API-ready services. RaaS can be configured for specific industries (legal, finance, healthcare) or business domains, creating optimized retrieval and prompt strategies for unique use cases.
Where is it used?
RAG – as a service + Generative AI use cases:
- Amazon Bedrock gives access to different large language models and makes it easy to integrate external knowledge sources.
- Vectara handles everything from data ingestion and text processing to embedding generation and vector database management, ensuring the results are relevant and accurate.
- Microsoft Azure AI Search helps with the retrieval part, which can then be paired with other services like Azure OpenAI for generating responses.
- Oracle is pushing RAG use cases, linking them with their own cloud services.
These platforms make it easier for companies to create applications driven by AI trends, such as chatbots and knowledge management systems, that can work with real-time, large-scale data.
Conclusive
Retrieval-Augmented Generation (RAG) as a Service (RaaS) combines the capabilities of Large Language Models with real-time data retrieval to enhance the accuracy and context of AI-generated responses.
These services eliminate the need for AI development companies to manage complex infrastructure for embedding generation, data integration, and vectorization. RaaS platforms typically offer industry-specific configurations, tailored to sectors like AI in Fintech, healthcare, legal, and finance, improving the relevance of the responses.
It offers data encryption and compliance with regulations, ensuring safe data handling. With API integrations, RaaS platforms easily connect with existing tools and systems, making it simpler for businesses to scale their AI applications.
To focus on developing intelligent, real-time AI development solutions that improve customer experiences and automate much of the heavy lifting, avail RaaS by hiring machine learning developers by tying up with Konstant Infosolutions. Request a free quote – https://www.konstantinfo.com/contact-us.php.