RAG (Retrieval-Augmented Generation) Defination, Design, Build and Deploy to RAG Business Product and Marketing
Definetion of RAG(Retrieval-Augmented Generation)
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 (AWS: https://aws.amazon.com/what-is/retrieval-augmented-generation/)
RAG (Retrieval-Augmented Generation) refers to a process where artificial intelligence models use external databases to obtain relevant information and generate more accurate responses. (https://www.meilisearch.com/blog/what-is-rag)
Retrieval-Augmented Generation (RAG) is an advanced AI framework that combines information retrieval with text generation models like GPT to produce more accurate and up-to-date responses. Instead of relying only on pre-trained data like traditional language models, RAG fetches relevant documents from an external knowledge source before generating an answer.
Retrieval-Augmented Generation (RAG) is an AI framework that improves LLM accuracy. Learn how to inject enterprise data into LLMs for more reliable responses.
Retrieval augmented generation, or RAG, is an architecture for optimizing the performance of an artificial intelligence (AI) model by connecting it with external knowledge bases. RAG helps large language models (LLMs) deliver more relevant responses at a higher quality. (https://www.ibm.com/think/topics/retrieval-augmented-generation)
Retrieval-augmented generation (RAG) is a powerful AI technique that combines information retrieval with text generation. Instead of relying solely on pre-trained knowledge, RAG pulls real-time data from external sources, ensuring more accurate and up-to-date responses. This makes AI models more reliable, especially for applications that require fresh and factual information. (https://bhavikjikadara.medium.com/)
Retrieval augmented generation (RAG) combines the advanced text-generation capabilities of GPT and other large language models with information retrieval functions to provide precise and contextually relevant information. This approach improves language models' ability to understand and process user queries by integrating the latest and most relevant data.
Retrieval-augmented generation is a technique for enhancing the accuracy and reliability of generative AI models with information from specific and relevant data sources.
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Retrieval-augmented generation is Advance and powerful of process technique or architecture optimizing of AI framework is using large language models (LLM) integration source by user queries and combines advanced text-generation with provide information retrieval .
Design of RAG (Retrieval-Augmented Generation)
- Access to updated information
- Factual grounding
- Contextual relevance
- Factual consistency
- Utilizes vector databases
- Improved response accuracy
- Multi-modal capabilities
How to Bulid of The RAG (Retrieval-Augmented Generation)
To build a RAG system, first prepare your data by collecting and chunking documents, then convert them into numerical embeddings. Next, store these embeddings in a vector database for efficient similarity searches. Finally, create a RAG pipeline where a user's query is embedded, used to retrieve relevant document chunks from the database, and then combined with the original query to augment a large language model (LLM) for a final answer
1. Prepare and index your data
- Collect and clean data: Gather the documents you want to use as a knowledge base.
- Chunk the data: Break down large documents into smaller, manageable pieces of text.
- Generate embeddings: Use an embedding model to convert each text chunk into a numerical vector representation.
- Store embeddings: Load these embeddings and their corresponding text into a vector database for fast similarity searching.
2. Build the retrieval and generation pipeline
- Embed the user query: When a user asks a question, use the same embedding model to create a vector for the query.
- Retrieve relevant chunks: Use the query embedding to search the vector database and find the most similar document chunk embeddings.
- Augment the prompt: Combine the user's original question with the retrieved text chunks to create a more detailed prompt. For example, you could add text like "Based on the following documents, answer the user's question".
- Generate the final answer: Send the augmented prompt to the LLM, which will use the provided context to generate a more accurate and relevant response.
3. Tools of Build & deploy the RAG
- Meilisearch is an intuitive yet powerful search engine that scales with your business. Our core aim with this product is to empower RAG pipelines, semantic search, and lightning-fast information retrieval for devs and product teams.
- LangChain structures workflows through prompts, tools, memory, and vector stores. If you’re building an AI agent, chatbot, RAG system, or document question-answering tool, you cannot go wrong with LangChain.
- RAGatouille is a lightweight Python package that brings ColBERT-style late interaction retrieval into real-world RAG pipelines. It’s open source, easy to install, and compatible with LangChain, LlamaIndex, and other frameworks.
- Verba, you get a user-friendly UI where you can upload documents and index them into Weaviate’s vector database. Verba’s chat interface guides non-technical users through document ingestion, chunking, vectorization, and chat-based querying.
- Haystack is an open-source Python framework developed by deepset for building production-grade RAG pipelines, AI agents, and semantic search systems.
- Embedchain is a minimal, open-source RAG framework for building ChatGPT-like apps over your data. It excels at simplifying ingestion, indexing, embedding, and querying into just a few lines of Python. This is perfect if you want to prototype or deploy lightweight bots.
- LlamaIndex (formerly GPT Index) is an open-source framework that helps connect external data to LLMs for building context-aware applications. It streamlines how you load, transform, index, and query data.
- MongoDB Atlas Vector Search brings semantic retrieval directly into your primary database, allowing you to store and query vector embeddings alongside application data.
- Pinecone is a managed, cloud-native vector database built for high-performance similarity search in AI applications, especially for RAG workflows. It offers hybrid search capabilities (dense + sparse vectors), serverless scaling, and global reliability, all accessible through a simple REST API.
- Vespa is an open-source, high-performance AI platform developed by Yahoo. It combines traditional and multimodal search, vector similarity, and machine learning ranking in a unified system optimized for real-time, large-scale RAG and retrieval applications.
Type of The RAG (Retrieval-Augmented Generation)
- Simple RAG (original) : refers to the most basic form of retrieval-augmented generation, where the AI system retrieves relevant documents from a knowledge base in a single step and uses that information to generate a response.
- Simple RAG with memory : In the context of RAG, memory refers to the AI system's ability to keep track of past interactions (such as past questions, answers, or retrieved documents). It does not just remember what was said, but understands how previous context can influence new searches.
3. Agentic RAG
4. Graph RAG
5. Self-RAG
6. Branched RAG
7. Multimodal RAG
4. Graph RAG
5. Self-RAG
6. Branched RAG
7. Multimodal RAG
8. Adaptive RAG
9. Speculative RAG
10. Corrective RAG
11. Modular RAG
12. Naive RAG
13. Advanced RAG
14. HyDE (hypothetical document embedding)
15. Corrective RAG
16. Fusion RAG
17.RadioRAG
Busineess and Marketing Product of The RAG (Retrieval-Augmented Generation)
Reference :
8 Retrieval Augmented Generation (RAG) Architectures You Should Know in 2025. URL: https://humanloop.com/blog/rag-architectures
14 types of RAG (Retrieval-Augmented Generation).
Exploring the Different Types of RAG in AI.
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