RAG techniques

Large language models (LLMs) generated at a large scale have transformed AI applications; however, they still have various drawbacks as their knowledge is static and can come only from their training data. This juncture is where retrieval-augmented generation (RAG) comes into play.

RAG adds power to generative AI models by interspersing real-time data retrieval, ensuring that the retrieval process produces a more accurate and timely output. However, RAG models come in different forms, suited primarily for different applications¹.

In this article, we explore various RAG techniques along with how they work, strengths and limitations of each RAG type and their usability in various use cases.

To improve the overall effectiveness and sustainability of RAG models, retrieval systems have evolved from naive RAG to advanced RAG and modular RAG to address challenges in performance, cost and efficiency. Let’s explore each RAG technique in depth.

Naive RAG is a basic implementation of retrieval-augmented generation, where retrieving information and generating responses are done without any optimizations or feedback. In this straightforward setting, the system retrieves relevant data based on a query, which is then simply fed into a language model (such as GPT) to generate the final answer².

Naive RAG relies on a rather straightforward three-step process for retrieval and content generation. The following steps explain how the retrieval process works:

1. Query encoding: The query made by the user is transformed into a high-dimensional vector by using an embedding model, capturing the semantic meaning of the entire query.
2. Document retrieval: A similarity search is performed by using this vector against repositories through vector databases retrieving the top-N documents relevant to the query. The knowledge base can be constructed from structured and unstructured data sources, such as open source datasets or enterprise datasets.
3. Response generation: The retrieved data sources are then served as additional context into a language model that synthesizes a coherent and informative response grounded in external knowledge ^{3, 4}.

Fig 1 illustrates the three-step process (encoding, retrieval and response generation) of how naive RAG works.

Naive RAG is best suited to scenarios where simplicity, speed and ease of deployment are paramount over advanced accuracy and flexibility. The simplicity of the architecture makes it ideal for building proof-of-concept applications and enabling fast testing of ideas without the burden of cumbersome model adjustments. For example, it can be effectively used in:

a. Customer support chatbots: Handling frequently asked repetitive question-answering scenarios by using LLM responses.

b. Summarization and information retrieval: Providing a basic level of summarization by using natural language processing techniques.

c. AI systems for enterprises: Quickly retrieving relevant data from repositories to answer common queries.

Although naive RAG is simple and fast, advanced RAG offers greater flexibility, scalability and performance, making it suitable for complex, real-world applications.  

Let’s understand what advanced RAG is and what key offerings it offers.

Advanced RAG combines the power of better retrieval and generation by using sophisticated algorithms—a series of ideas, such as rerankers, fine-tuned LLMs and feedback loops. These improvements bring enhancements in accuracy, adaptability and performance that make these models the better choices for more complex and production-grade applications⁵.

Advanced RAG works as a sequential step-based process as follows:

1. Query processing: Upon the reception of a user query, it is transformed into a high-dimensional vector by using the embedding model that captures the semantic meaning of the query.

2. Document retrieval: The encoded query traverses a huge knowledge database that provides hybrid retrieval by using both dense vector search and sparse retrieval that is, semantic similarity and keyword-based search. The results thus introduce semantic keyword matches into the retrieved documents.

3. Reranking retrieved documents: The retriever gives a final score based on context and in relation to the query retrieving the documents.

4. Contextual fusion for generation: Because each document is encoded differently, the decoder fuses all encoded contexts to ensure that the generated responses have coherence with to the encoded query.

5. Response generation: The generator of advanced RAG, usually an LLM, such as the IBM Granite™ model or Llama, provides the answer based on the retrieved documents.

6. Feedback loop: As advanced RAG uses various techniques like active learning, reinforcement learning and retriever-generator cotraining to continuously enhance its performance. During this phase implicit signals occur, such as clicks on retrieved documents that infer relevance causing explicit feedback that includes corrections or ratings for further application during generation. Hence, over the years, these strategies improve the retrieval as well as the response generation processes so that more accurate and relevant answers can be produced⁶.

   Fig 2 illustrates the stepwise process of how advanced RAG works.

Advanced RAG is extremely versatile for a variety of applications across industries due to the capability for real-time information retrieval and dynamic, accurate and context-based responses. Its application varies from enabling customer service to bringing about relevant information thereby improving decision making and adding enhancement to personalized learning experiences. The improved retrieval and generation through advanced RAG makes it practical for applications in real time, but scalability and usability are below par for production level use cases.

Modular RAG is the most advanced variant of RAG, where the information retrieval and the generative model work in an open, composable linear pipeline-like architecture. This approach allows different use cases to perform better with customizability and scalability.

By disaggregating the act of RAG into modules, one can better adapt, debug and optimize each component independently. Now let's see how modular RAG works in real action⁷.

1. User query processing: The first step is the user submitting a query, such as, "What is the most trending book in the market these days?" A query processing module then transforms the input that might include rephrasing the query, removing ambiguities and performing semantic parsing to provide a more informed context before it is submitted for retrieval.

2. Retrieval module: The retrieval module processes the query on the vector database or knowledge base to obtain relevant documents. It performs retrieval by using the embedding-based similarity paradigm.

3. Filtering and ranking module: The retrieved documents are then filtered by using metadata, recency or relevance. And a reranking model scores and prioritizes the most useful information.

4. Context augmentation module: This module feeds retrieved information with knowledge graphs, embeds structured data coming from databases and APIs and applies retrieval compression to achieve the best content retrieval.

5. Response generation: The LLM processes the user query along with the retrieved context to generate a coherent and accurate response, minimizing hallucinations and ensuring relevance.

6. Post-processing module: This module ensures accuracy through fact-checking, improves readability with structured formatting and enhances credibility by generating citations.

7. Output and the feedback loop: The final output of the response is presented to the user while a feedback loop is created from their interaction to assist with refining retrieval and model performance over time.

   Fig 3 illustrates the stepwise process of how modular RAG works.

Advanced RAG fits in use cases where the application requires immense customization, for instance, domain-specific retrieval and ranking techniques. Scalability and maintainability are important for applications that involve large-scale systems and there is continuous experimentation with different retrieval models and strategies⁸.

While naive RAG is straightforward and quick, modular RAG—often built with frameworks such as LangChain—provides enhanced flexibility, scalability and performance, making it more suitable for intricate, real-world applications. Advanced RAG improves accuracy by retrieving real-time, context-specific information that helps minimize errors. It adapts dynamically, incorporating user feedback through active learning and reinforcement learning (RLHF). Furthermore, it bolsters domain-specific knowledge by integrating specialized databases. It also optimizes the LLM’s context window by fetching only the most pertinent data, thereby enhancing efficiency. Nonetheless, advanced RAG systems encounter challenges such as higher compute demands and latency due to both retrieval and generation processes. They require significant resources to manage extensive knowledge bases and involve complex implementation and maintenance—particularly when fine-tuning retrievers, ranking models and response generators. This space is where modular RAG architectures that are developed using LangChain, excel. Their modular design allows for flexible customization, enabling individual components—such as retrievers, rankers and generators—to be fine-tuned or swapped out independently. This method enhances maintainability by making debugging and updates easier without disrupting the entire system. Scalability is achieved by distributing modules across various resources while costs are managed by optimizing retrieval processes and minimizing LLM usage ^{9, 10}.

Active development in retrieval systems that leverage advanced prompt engineering techniques and fine-tuning methods to enhance RAG models for high-precision content generation are going on to ensure better performance and scalability.

Future advancements in self-RAG approaches, multimodal AI models and improved metrics will continue to refine the retrieval process, ensuring better handling of additional context in natural language interactions.