RAG has significant advantage over traditional generative models that rely solely on pre-trained data. RAG systems incorporate a retrieval mechanism that dynamically fetches relevant information to improve the accuracy and reliability of responses.

Every component discussed in the previous blog can be divided into four main sections:

Data Store: Contains vector embeddings of the knowledge base for efficient retrieval.
Retriever: Responsible for fetching relevant documents based on the query.
LLM Inference: The core model that generates responses based on retrieved data.
Knowledge Base: The source of truth containing structured or unstructured information.

Data store and Knowledge base

The foundation of any RAG system is its knowledge base, which serves as the source of truth for generating responses. It contains structured or unstructured information that acts as a reference point for the system. A knowledge base can be either static, such as an academic corpus or company documentation, or dynamic, constantly updated with new data like news articles or social media feeds. A well-maintained knowledge base ensures that the system produces accurate and up-to-date responses, making it a critical component of a RAG framework.

Once a knowledge base is established, it needs to be structured in a way that allows for efficient searching and retrieval. This is where the data store comes into play. The textual data from the knowledge base is converted into vector embeddings using models like OpenAI’s Ada, Facebook’s FAISS, or other embedding techniques. These embeddings capture the semantic meaning of text, making it easier to search for similar content.

The vector embeddings are then indexed in a database optimized for retrieval, such as Pinecone, FAISS, Qdrant, Weaviate, or ChromaDB. This allows for fast and accurate searches when retrieving relevant information. The efficiency of the data store determines the speed and relevance of document retrieval, making it a critical component of a RAG system.

Retrieval

The retrieval mechanism is what sets RAG apart from traditional LLMs. Instead of relying only on the model’s training data, RAG dynamically fetches relevant documents from the data store before generating a response. When a user asks a question, the system converts the query into a vector representation and searches for the most relevant documents in the indexed data store. The retrieved documents are then ranked based on their relevance to the query using similarity metrics such as cosine similarity, dot product, or more advanced ranking algorithms. Some systems apply additional filters, such as keyword matching or domain-specific constraints, to refine results before passing them to the LLM. By integrating retrieval, RAG significantly enhances response quality, particularly for domains requiring precise, factual, or up-to-date information.

Large Language Model (LLM)

Once relevant information is retrieved, it is passed to the LLM, which generates a response based on both the query and the retrieved knowledge. The LLM takes the user’s query and the retrieved context to produce a coherent and contextually relevant answer. Most LLMs, such as GPT-4, Llama, or Claude, are pretrained on massive datasets, enabling them to understand and generate human-like text. Some RAG implementations fine-tune the model with domain-specific data to improve performance for specialized applications, such as legal, healthcare, and finance. By incorporating LLMs and the retrieval of external knowledge, RAG offers a more reliable and scalable approach to AI-driven applications.

Data Pipeline and Application

A RAG system can be divided into two main parts: the data pipeline and the application. The data pipeline is essential for keeping the vector store up to date. This involves periodically updating embeddings and applying chunking techniques to break large documents into manageable pieces for better retrieval efficiency.

The application, on the other hand, is responsible for processing user queries. It uses the same embedding model as the data pipeline to ensure consistency in vector representations. Additionally, incorporating chat history into queries allows the model to generate responses with greater context awareness, leading to more meaningful and relevant interactions.

Conclusion

RAG represents a significant advancement in AI and NLP by combining retrieval and generation. The four key components, retriever, data store, LLM inference, and knowledge base work together to provide accurate, relevant, and context-aware responses. RAG is a very young technologies and there are several research and advancements taking place very day. Some of these are in the field of optimizing retrieval mechanisms to improving vector indexing and fine-tuning LLMs for specific domains.

Furthermore, evaluating a RAG system’s performance is essential to ensure that it delivers high-quality results. In our next blog, we will explore various evaluation metrics and methodologies to measure the effectiveness of RAG implementations, helping developers build more reliable and impactful AI systems.

RAG: Putting the pieces together (Part - 3)