In 2025, RAG has become one of the most used technique in the domain of Large Language Models. In fact, one can assume that no LLM powered application doesn't use RAG in one way or the other. Here are 6 use cases that RAG forms a pivotal part of
If you're interested in finding out more about retrieval augmented generation, do give our blog a read - Context is Key: The Significance of RAG in Language Models
Top 6 RAG Use Cases:
Use Case #1: Smarter Document Question-Answering Systems
By providing access to proprietary enterprise document to an LLM, the responses are limited to what is provided within them. A retriever can search for the most relevant documents and provide the information to the LLM. Check out this blog for an example.
Use Case #2: Context-Aware Conversational Agents
LLMs can be customized to product/service manuals, domain knowledge, guidelines, etc. using RAG. The agent can also route users to more specialised agents depending on their query. SearchUnify has an LLM+RAG powered conversational agent for their users.
Use Case #3: Real-Time Event Commentary AI
Imagine an event like a sports or a new event. A retriever can connect to real-time updates/data via APIs and pass this information to the LLM to create a virtual commentator. These can further be augmented with Text To Speech models. IBM leveraged the technology for commentary during the 2023 US Open
Use Case #4: Hyper-Personalized Content Generation
The widest use of LLMs has probably been in content generation. Using RAG, the generation can be personalized to readers, incorporate real-time trends and be contextually appropriate. Yarnit is an AI based content marketing platform that uses RAG for multiple tasks.
Use Case #5: Next-Gen Recommendation Engines
Recommendation engines have been a game changes in the digital economy. LLMs are capable of powering the next evolution in content recommendations. Check out Aman's blog on the utility of LLMs in recommendation systems.
Use Case #6: Supercharged Virtual Assistants
Virtual personal assistants like Siri, Alexa and others are in plans to use LLMs to enhance the experience. Coupled with more context on user behaviour, these assistants can become highly personalized.
If you're someone who follows Generative AI and Large Language Models let's connect on LinkedIn
Also, please read a free copy of my notes on Large Language Models
I write about Generative AI and Large Language Models. Please follow to read my other blogs
