RAG (Retrieval-Augmented Generation) is a framework in Artificial Intelligence that combines retrieval-based methods with generative models (like GPT or BERT) to produce more accurate, informative, and context-aware responses.
RAG by by NVIDIA:
Retrieval-augmented generation is a technique for enhancing the accuracy and reliability of generative AI models with information from specific and relevant data sources. In other words, it fills a gap in how LLMs work. Under the hood, LLMs are neural networks, typically measured by how many parameters they contain. An LLM’s parameters essentially represent the general patterns of how humans use words to form sentences.
RAG by G-Cloud:
RAG (Retrieval-Augmented Generation) is an AI framework that combines the strengths of traditional information retrieval systems (such as search and databases) with the capabilities of generative large language models (LLMs). By combining your data and world knowledge with LLM language skills, grounded generation is more accurate, up-to-date, and relevant to your specific needs.
RAG by AWS:
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.
May be I can explain RAG as ...
Retrieval-Augmented Generation (RAG) is a hybrid AI architecture.
It augments a language model with an external knowledge base (retrieved at runtime).
This helps the model access up-to-date or domain-specific information that may not be in its training data.
How it Works?
Query → User asks a question.
Retrieval → A retriever (like FAISS, Elasticsearch, etc.) fetches relevant documents from a knowledge base.
Augmentation → Retrieved documents are appended to the prompt for a generative model.
Generation → The generative model (e.g., GPT, T5) produces an answer using both the query and the retrieved context.
Real-World Use Case Example
Use Case: Customer Support Bot for an Insurance Company
Problem:
The chatbot should answer policy-related questions like:
“What does my health insurance plan cover for cancer?”
Traditional model might say:
“Health insurance may or may not cover cancer based on the plan.”
RAG-based chatbot does:
Retrieves actual policy documents from the company’s internal knowledge base.
Generates answer like:
“According to your Health Secure Plan (Policy ID: XXZZ2025), cancer benefits cover up to ₹500,000, including pre and post-natal expenses.”
- Personalized
- Accurate
- Trustworthy
When to Use RAG?
When information is frequently changing (e.g., news, prices, health guidelines).
When custom domain knowledge is critical.
When you want to combine accuracy with natural language fluency.
When custom domain knowledge is critical.
When you want to combine accuracy with natural language fluency.
Agentic RAG
Agentic AI and LLM agents have been around for a few months, and the idea is simple: AI systems that don’t just give answers but actively assist with tasks, adapt to new information, and work independently when needed. The challenge, though, is making these systems reliable and up-to-date, especially in areas where the information changes all the time.
RAG was one of the first approaches to tackle this problem. It combines two things: the ability to fetch real-time, relevant data (retrieval) and the power to generate responses using that data (generation). As soon as agentic AI became a focus, RAG quickly stood out as a natural fit. It gave AI systems the ability to stay current and respond with information that made sense for the situation.
What are RAG agents?
RAG agents are AI tools designed to do more than retrieve and generate—they’re built for doing specific tasks. Think of them as goal-oriented assistants that know where to find the information they need and how to use it. Instead of generic answers, they’re tailored for real-world situations.
For example:
A RAG agent in customer support doesn’t only tell you the refund policy; it finds the exact details for your specific order.
In healthcare, a RAG agent doesn’t only summarize medical studies; it pulls the most relevant research based on a patient’s case.
So, if an LLM-based RAG would only answer questions, RAG agents fit into workflows and make decisions based on fresh, relevant data.
A RAG agent in customer support doesn’t only tell you the refund policy; it finds the exact details for your specific order.
In healthcare, a RAG agent doesn’t only summarize medical studies; it pulls the most relevant research based on a patient’s case.
So, if an LLM-based RAG would only answer questions, RAG agents fit into workflows and make decisions based on fresh, relevant data.
RAG agent frameworks (Most popular in 2025)
Some of the famous agentic RAG frameworks are LangChain, LlamaIndex Agents, DB GPT, Quadrant Rag Eval, MetaGPT, Ragapp, GPT RAG by Azure, IBM Granite, Langflow, AgentGPT, CrewAI etc.
RAG vs. Semantic Search
| Feature | RAG (Retrieval-Augmented Generation) | Semantic Search |
|---|---|---|
| Definition | Combines document retrieval with a generative LLM to answer queries in natural language | Retrieves documents or chunks that semantically match a query (no generation) |
| Output | Synthesized, human-like response using retrieved content | List of relevant documents or passages |
| Use Case | Question answering, summarization, chatbots | Search engines, document finders, internal knowledge lookup |
| Model Role | Uses both retriever + generator (e.g., vector search + GPT) | Only uses retriever (e.g., vector embeddings + similarity search) |
| Latency | Higher (retrieval + generation step) | Lower (just retrieval) |
| Explainability | Moderately explainable (shows retrieved docs and generation) | High explainability (results are directly retrieved) |
| Example Prompt | "Summarize all recent changes in our HR policy." | "Show me documents related to recent HR policy updates." |
Important Update as on 2nd October 2024:
Cohere.command, cohere.command-light, meta.llama-2-70b-chat (and few more) AI models got retired (and in the last few months), so any application using these AI models will not work and give a response error message “Entity with key <model-name> not found” or “Error 400 <model-name> does not support TextGeneration”, Please see the list of models already retired or sooner to retire.
