Retrieval-Augmented Generation (RAG): How It Works and Why It Matters

What is Retrieval Augmented Generation (RAG)? 

The landscape of artificial intelligence involves constantly evolving techniques to overcome the challenges posed by traditional applications. Retrieval-augmented generation is a key tool used to subdue the limitations of large language models (LLMs) as they are trained on real-world data, provide the latest information, and ensure that the results generated are relevant as per the context. RAG combines the techniques of text generation by generative AI services and up-to-date information retrieval from external data sources or vast datasets. This aids in avoiding the relay of outdated, false, or insufficient information embedded in an LLM. Sometimes, generative AI systems produce false or misleading outputs, incorrect facts, etc., that may deviate from reality and are obtained from non-existent sources. This phenomenon is referred to as Gen AI hallucination, which can be overcome using retrieval augmented generation, RAG. The market size for RAG is valued at US $1.96 billion as of 2025 and is expected to reach an approximate value of US $40.34 billion by 2035, increasing at a CAGR of 35.31% during this time period. 

Increasing market size of retrieval augmented generation RAG from 2020 to 2030 

Components of Retrieval Augmented Generation 

To understand more about the working of RAG, one needs to go through its components in a detailed manner, where each plays an important role in its functioning to ultimately provide accurate results: 

Retriever 

This component is responsible for searching relevant documents that match the user’s prompt or query through huge databases and external sources. For efficiency purposes, the user prompt and documents in datasets are converted into mathematical and textual representations called vectors by an embedding model. The query processing module handles the preparation of the user query by converting the raw input into an embedded form for relevant document retrieval. The model ensures that the vector is able to convey the semantic meaning of the text for accurate matching. Here, semantic refers to the manner in which a system created by AI development services understands and generates content in terms of the context and relation between concepts, words, phrases, sentences, etc. After conversion, the document embeddings are stored in a vector database for quick retrieval and comparison with the query vector for relevant matches. 

This task performed by a retrieval augmented generation system can be done by a dense retriever such as DPR that utilizes neural networks to encode the prompt and the documents in databases into a vector space. It then measures the similarity between them by using dot products or cosine similarity. Another type of retriever is a sparse retriever, such as BM25, that matches the keywords or n-grams from the prompt with the documents present in external sources in a manner similar to a search engine. The hybrid retrieval system can balance both these methods by combining exact matches with the semantic understanding for extensive information retrieval. Post-retrieval, irrelevant and low-quality data is filtered out, so that only the most useful information can be utilized in the final output. 

Generator 

After retrieving the required information, the generator component blends the same with the user query and generates an informative textual output. It may employ fusion strategies upon retrieval to do the same, such as early fusion, where the query is concatenated with the retrieved documents prior to result generation, or late fusion, where every document is retrieved separately and then combined to form a final response, or cross-attention, where the model attends to both the input query and the retrieved document simultaneously during response generation. The contextualization module makes sure that the response generated is relevant to the context by considering previous or related queries for continuous interaction. 

It is usually an LLM such as Text-to-Text Transformer (T5), which converts the input sequences that is query and context into result sequences or answers; GPT-3 which generates coherent text for any prompt; or BART which is a denoising autoencoder-based sequence-to-sequence model that can generate summaries and question-answer texts. The synthesis module makes sure that the output generated, and the retrieved information are interconnected, such that the result is consistent, appropriate in terms of the context, and logical. 

Workflow of Retrieval augmented generation RAG for handling multiple documents 

Working of Retrieval Augmented Generation 

Given below is a brief account of the step-by-step process of the working of RAG to gain insights on its working: 

1. Query Embedding: The initial step is to convert the user’s question or prompt into a vector form. The query is passed through an embedding model such as BERT or DPR and turned into a vector that represents its semantic meaning in a high-dimensional space. 

2. Document Search: The query vector is then searched against vast vector datasets, external data sources, or pre-defined knowledge bases for semantically similar documents and information to retrieve relevant context. The documents collected by the retriever component of retrieval augmented generation, RAG, are ranked as per their relevancy to the prompt by calculation of a similarity score. 

3. Contextualization: The top-ranked documents or most relevant information collected from databases are passed onto the generator component for incorporating the retrieved knowledge. In response generation. 

4. Text Generation: The generator LLM, such as T5 or GPT-3, utilizes the received context alongside the original input prompt to generate a detailed output or answer backed by external sources. 

Applications of Retrieval Augmented Generation 

Below mentioned are some of the real-world applications of RAG: 

• Text Generation: RAG can generate creative and accurate content such as detailed reports, articles, blogs, etc., consisting of facts. 

• Educational Systems: It is useful for developing question-answering systems that provide up-to-date research and educational content, such as relevant summaries, explanations, etc. in detail. 

• Chatbots: It improves the efficiency of conversational agents by assisting them in gathering content in real-time and providing accurate responses to customer queries that are relevant to the context. 

• Diagnostics: Retrieval augmented generation, RAG, aids healthcare professionals in taking treatment-related decisions and supporting medical diagnosis by retrieving the latest and pertinent data. 

• Search Engines: It enhances document retrieval by search engines, thus supporting generative AI in retail for generating a coherent response to user queries by gathering answers from knowledge bases in place of a list of outcomes from websites. 

• Legal Assistance: In the legal sector, RAG is useful for accessing suitable case studies, papers, and other legal documents for creating relevant advice as per obtained data. 

• Translation: It obtains context from multilingual sources for creating accurate translations for complex and specialized topics that may require domain-specific knowledge. 

• Debugging: Retrieval augmented generation, RAG, aids developers in obtaining code snippets, necessary documentation for generating code completions, explanations, and solutions against bugs, thus enhancing development workflow. 

• Knowledge Management: It can automatically populate and maintain knowledge bases while retrieving data and generating reports, summaries or insights. It is especially useful for conversational agents and chatbots. 

• Others: It can be extended to handle applications that require multimodal or multiple types of data. It can be modified further to retrieve videos, images, audios and text to generate relevant responses. 

Unlock the Full Potential of AI Solutions with KritiKal 

RAG is a powerful and versatile solution that can be tailored and applied across domains to revolutionize domain-specific interactions between users and artificially intelligent systems. It is a valuable tool that grounds responses as per real and latest research papers, sources, or documents, thus reducing AI hallucinations and increasing accuracy. Its functioning is integrated with reasoning and external context for providing factual and logical responses that enhance the interaction quality.  

The current major limitation of retrieval augmented generation includes increased complexity in terms of development and maintenance due to retriever and generator components. Another challenge is related to the management of large knowledge bases, which can be difficult to scale as it requires more resources for developing real-time applications. AI systems such as these constantly face limitations posed by the introduction or amplification of biases, which in this case may occur during the retrieval process, thus affecting the quality and neutrality of generated responses. These systems may not be equipped to handle contextually large requirements, triggering fragmented responses. Furthermore, retrieving data from large datasets can be a cumbersome and time-consuming process leading to delayed responses. 

KritiKal has assisted major businesses across the globe in overcoming such challenges and introducing RAG-based solutions in their offerings to gain a competitive edge in the market. We aid in developing solutions that are capable of indexing text, images, and videos, textual and video search queries; audio-to-text generation; video transcripts, tag and caption generation; word cloud, and multimodal embedding generators. We utilize RAG platforms such as LlamaIndex, LangChain, equipped with LLMs like Mistral 8B, etc. to handle multiple types of documents and follow a comprehensive reranking approach to ease domain-specific applications. We can assist in the development of state-of-the-art RAG-based solutions powered by LLMs as well as in implementing generative AI in cybersecurity. Please get in touch with us at sales@kritikalsolutions.com to know more about our RAG-based solutions and realize your contemporary requirements. 

Ritik Bhaise

Ritik Bhaise currently works as a Software Engineer at KritiKal Solutions. He is proficiently skilled in Node.js, Redux.js, React.js, front-end development, C#, ASP.NET, SCCS, SQL, CI/CD, MVC, and more. With his extensive analytical skills and ability to work efficiently in teams, he has assisted KritiKal in delivering various projects to some major clients.