What is Retrieval Augmented Generation (RAG)? The Key to Smarter, More Accurate AI

Businesses that provided automated customer support, content generation, or AI-driven services relied on pre-trained models, including large language models (LLMs) like GPT-2, which gained traction around 2018. These models generated responses based solely on the data on which they were trained. While effective for handling static tasks, these models had limitations for dynamic scenarios. They could not access updated information, making them less adaptable to changes in business needs, like product launches or policy updates. This limitation persisted until 2020 when retrieval-augmented generation (RAG) was introduced. RAG allowed users to retrieve information from external knowledge sources, improving relevance and accuracy in AI-generated responses.

For instance, if you built a translation tool using an LLM, the model would only provide translations based on the training data it had seen. If a new term or phrase emerged after the model was trained, the translation tool wouldn’t have access to this information and might produce inaccurate or outdated translations. This could hinder effective communication with international clients or partners, resulting in misinterpretations of key messages, marketing materials, or contracts. With the introduction of RAG, companies can now structure their LLMs to retrieve relevant information from external knowledge sources, ensuring that responses remain accurate and up to date. Besides retrieving the latest information, RAG could also provide specialized information, such as accessing a company’s support tickets (to provide tailored responses based on previous inquiries), where the focus is on retrieving detailed and contextually relevant data. Read on to learn about RAG, how it works, and its benefits.

What is RAG?

RAG is a technique in natural language processing and artificial intelligence that combines two components: information retrieval and text generation.

In this approach, external documents or data sources from web pages/research papers/proprietary company datasets are first retrieved in response to a query (based on a user’s input). This retrieval process uses a vector database, where the data is pre-processed into vector embeddings. These embeddings capture *semantic meanings, allowing for efficient search and retrieval of the most relevant documents based on the query. Then, generative models like generative pre-trained transformers (GPT) or bidirectional and auto-regressive transformers (BART) use the retrieved information to produce a coherent and relevant response. RAG builds more informed and accurate outputs, combining existing knowledge bases with generative capabilities.

*Semantic meaning deciphers the relationships between words and their usage in context. For example, the words “bank” (as a financial institution) and “bank” (as the side of a river) have different meanings depending on the context.

How does a RAG work?

Input query

The user initiates this step by providing a query through a user interface like a chatbot, search bar, or API request. Technically, the input is captured and processed by the system, mostly using natural language processing (NLP) techniques like tokenization (breaking down the query into smaller units like words or phrases), stemming/lemmatization (reducing words to their base forms), and named entity recognition (NER) (identifying key entities like dates, names, or locations) to understand the context and intent of the query.

At this stage, the RAG system embeds the query into vectors — converting the text into numerical representations that can be used for document retrieval. These vectors enable the RAG system to perform similarity searches against a vector database provided by the organization, which stores pre-embedded document representations. This query embedding step allows the RAG system to perform vector-based similarity searches against the document corpus. The input query is passed into the RAG framework via REST APIs or integrated systems that accept text inputs from web applications, mobile apps, or other platforms for further processing.

Document retrieval

Once the list of documents is identified based on relevance to the user’s query, the system searches within a pre-built knowledge base, which could include internal company documents, databases, or pre-scraped external repositories (such as Wikipedia). The system compares the query embeddings with document embeddings, applying vector scoring techniques to rank and retrieve the most relevant information. This process ensures that only the most pertinent documents are selected. The retrieved documents provide factual data or context to support the response.

Information ranking

The retrieved contexts can be in any one or a combination of the formats—structured data (text, JSON, or HTML) or unstructured data (raw text data, scanned documents, or multimedia files like images, audio, and video) or other formats (PDFs, CSV files, or XML).

Different ranking techniques are applied depending on the source of the retrieval results to ensure that the most useful and accurate information is prioritized. The ranking is done by assessing how well the retrieved documents match the query. In an e-commerce fraud detection system, you might have retrieved transaction records in JSON format that are ranked using anomaly detection techniques, and unusual spending patterns are prioritized to identify potentially fraudulent activities based on the query.

Response generation

The retrieval results are next passed to the generative models (like GPT-4 or Mistral) through document concatenation or embedding injection, depending on the system architecture. The generation process combines contextual embeddings (numerical representations of words, sentences, or passages) from the retrieved content, self-attention mechanisms (e.g., the transformer architecture) to focus on relevant information, uses decoding techniques (like beam search or sampling techniques), and the model’s pre-trained knowledge (language patterns, facts, and syntax) to generate a relevant response.

Final output

The system generates a user-friendly response by formatting the text into structured outputs, such as plain text, HTML, or JSON. It then performs post-processing to ensure clarity and coherence. Finally, the response is integrated into the application interface and delivered through APIs or dynamic web calls, making the information easily accessible and as accurate as possible.

The system generates a user-friendly response by formatting the text into structured outputs like plain text, HTML, or JSON. It then performs post-processing to ensure clarity and coherence. Finally, the response is integrated into the application interface and delivered through APIs or dynamic web calls, ensuring that information is easily accessible and accurate.

Benefits of RAG

You can access dynamic, context-rich responses with RAG, ensuring your applications stay up-to-date and relevant. It adapts quickly to new information, and builds trust by delivering accurate, well-sourced information.

• Improved accuracy: By combining retrieval with generation, RAG improves the accuracy of responses. Instead of relying solely on a model’s pre-trained knowledge, it pulls relevant information from external sources, making responses more factually grounded. For example, when asked about recent events like news or scientific research, a RAG workflow can fetch up-to-date details from trusted databases.

• Reduced hallucinations: Generative models alone can sometimes produce inaccurate responses, known as AI hallucinations. RAG minimizes this by anchoring outputs to retrieved documents, ensuring the information is drawn from factual sources rather than purely generated. For example, in educational platforms, RAG improves generative AI by delivering accurate, fact-checked answers to student queries.

• Better handling of specialized topics: RAG is an excellent technique for niche or technical topics where the model’s training data might be limited. Pulling from domain-specific documents generates more accurate and detailed responses, such as retrieving medical papers to answer healthcare-related queries.

• Scalable knowledge integration: You can easily scale RAG systems to include more extensive and diverse datasets. As your knowledge base grows, so does the system’s ability to generate more informed and relevant responses, making it adaptable to various industries or applications. For instance, in conversational AI platforms, RAG can incorporate diverse data sources, allowing the system to improve its understanding and provide more accurate responses across various topics.

RAG vs fine-tuning vs prompt engineering

All three methods—RAG, fine-tuning, and prompt engineering—are designed to improve the performance of AI models. However, they vary in approach:

• RAG dynamically augments model responses by pulling in data but still relies on prompt engineering for query formulation.

• Fine-tuning changes the model itself by retraining it on new data to specialize it for specific tasks.

• Prompt engineering aims to get the best out of an unchanged, pre-trained model.

These methods are not mutually exclusive. For example, you can combine these three approaches to create an AI agent or chatbot, where RAG helps the chatbot pull real-time product information, fine-tuning adapts the underlying LLM to make the chatbot more skilled at answering domain-specific questions, and prompt engineering would ensure that the chatbot asks users the right questions and interprets their requests accurately.

Challenges of RAG

While RAG offers a dynamic solution, it also presents challenges in data consistency and scalability. Organizations may struggle with integrating diverse data sources, as varying formats and quality can lead to inconsistencies.

Additionally, scaling RAG to handle large, diverse datasets might introduce increased complexity and maintenance efforts, while handling biased or incomplete data can distort responses and affect overall accuracy.

Data reliability

When using RAG, you may face challenges with the reliability of the data provided to the system. If outdated or incorrect information is supplied to the knowledge base or training dataset, it might lead to inaccurate responses. For example, while building an analytics tool, if the data source is not regularly updated, the insights generated might be based on old information, affecting decision-making.

Latency issues

During the document retrieval and ranking process, RAG searches for relevant data from multiple sources, which takes time, especially if the sources are large or require API calls to third-party databases. Once the documents are retrieved, ranking them based on relevance might slow the process since complex ranking algorithms (like TF-IDF or BERT) are used. For instance, retrieving multiple external records for validation in fraud detection tools may delay response times, making it difficult to act on critical threats in real-time.

Managing unstructured data

Users are responsible for converting unstructured data (such as images, audio, or raw text) into structured formats—like text, JSON, or feature vectors—to become part of the external knowledge source used in the retrieval process. This task might involve complex steps, including OCR for scanned documents, speech-to-text for audio, or image recognition for visuals, all of which might introduce potential inaccuracies and require careful handling.

For example, a developer building a customer support analytics tool or bug-reporting system that processes user reviews would need to manage data inconsistencies and preprocessing challenges. OCR for scanned documents can introduce errors, such as misinterpreted characters, while raw user reviews may contain informal language that’s difficult to standardize. These inconsistencies make it challenging to convert the data into a structured format suitable for ranking, which might lead to inaccurate evaluations or require additional steps to clean and format the data properly.

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