In the article “What are Large Language Models (LLM)” we introduced Large Language Models (LLM), explaining their operation and main applications. We also discussed Prompt Engineering techniques to optimize the performance of LLMs.
In this article, we want to explain what RAG is and how to use it to optimize LLMs.
Retrieval-Augmented Generation (RAG)
The RAG method (Retrieval-Augmented Generation) combines data within a large language model with selected external data sources, such as specific repositories, text collections, and existing documentation. These resources are segmented, indexed in a vector database, and used as references to provide more accurate answers.
RAG is extremely useful because it allows the language model to use specific real-time information from one or more reliable sources. This approach offers significant savings by ensuring an optimal experience without the need to retrain or fine-tune models.
Additionally, it uses fewer resources because, after sending the query to the language model, it only returns the most relevant information, avoiding long and unfocused documents.
How the RAG Pipeline Works
The pipeline of a RAG system is divided into several stages, each crucial to the effectiveness of the overall process:
- Data Ingestion: Data is collected from various sources such as databases, documents, or live feeds. This data is pre-processed and transformed into formats compatible with the embedding system, which converts them into numerical vector representations.
- Embedding Generation: The transformed data is converted into high-dimensional vectors, representing the text in a numerical format understandable by the system.
- Storage in Vector Archives: The generated embeddings are stored in specialized vector databases, optimized for quickly handling and retrieving vector data.
- Information Search and Retrieval: When a user sends a query, the RAG system uses the vector representations to conduct efficient searches. It identifies the most relevant information by comparing the query vector with those stored in the databases.
- Response Generation: Using the user’s prompt and the retrieved data, the language model generates a complete response. This process combines the internal knowledge of the model with the retrieved external information, enhancing the accuracy and relevance of the responses.
- External Data Update: To keep information up-to-date, the system periodically updates documents and their embedding representations. This can be done through real-time automatic processes or periodic batches.
RAG and Indexing
Before effectively using a RAG system, it’s essential to have a solid data indexing process. This involves organizing and storing information in a Vector DB. These databases store information as numerical vectors representing the semantic meaning of the data. Indexing allows for the quick retrieval of relevant information, essential for the subsequent retrieval phase.
Vector DB: The Key to RAG
A fundamental component of the RAG pipeline is the Vector DB, a specialized database designed to manage and search semantic information in vector form. These databases are essential for RAG operation for several reasons:
- Semantic Representation: Vector DBs store information as numerical vectors representing the semantic meaning of the data. This contrasts with traditional databases that store data in structured or unstructured formats without capturing the intrinsic meaning.
- Efficient Search: Thanks to vector representation, Vector DBs allow for similarity-based searches. When a query is transformed into a vector, the database can quickly compare this vector with stored vectors to find the closest matches. This makes searching extremely efficient and precise.
- Scalability: Vector DBs are designed to handle large volumes of data. They can scale to contain millions or billions of vectors, ensuring that searches remain fast even as data volumes grow.
- Flexibility: These databases can be used for a wide range of applications, from document search to product recommendation, thanks to their ability to understand and compare the meaning of data.
User Query
The RAG pipeline begins with user input. The user asks a question in natural language, which the system must interpret and transform into a representation that can be processed by artificial intelligence models. This transformation occurs through vectorization, a process in which the question is converted into a numerical vector that captures the semantic meaning of the query.
For example, if a user asks, “What are the benefits of RAG?” the system translates this question into a vector representing the keywords and context of the query. This vector is then used to search for matches in the knowledge database.
Retrieval
Once the user’s query has been vectorized, the next step is retrieval. In this phase, the system compares the query vector with the vectors in the Vector DB. Using similarity measures, the system identifies the data chunks (e.g., paragraphs or document sections) that are most relevant to the query.
Retrieval is a critical phase because it determines the quality of the information used to generate the response. If the retrieved chunks are not relevant, the final response may be inaccurate or unhelpful. Therefore, the effectiveness of retrieval depends on the search engine’s accuracy and the quality of the indexing.
Augmentation
In the augmentation phase, the data chunks extracted during retrieval are combined with the user’s original query to create a response template. This phase can be seen as data preparation for the generative model.
The response template includes the key facts extracted from the documents and organizes them in a format that facilitates the generation of the final answer. This augmentation process ensures that all relevant information is considered and that the response is contextually accurate and complete.
Generation
The final phase of the RAG pipeline is response generation. In this phase, the generative model (typically an LLM) takes the response template and processes it to produce a coherent and natural response to the user’s query.
The generative model uses the facts and contexts provided during the augmentation phase to construct a response that not only directly answers the user’s question but does so in a way that is easy to understand. This response is formulated using advanced natural language generation algorithms, allowing the system to produce texts that appear human-written.
Advantages of RAG
Integrating external and verifiable data through the RAG architecture offers multiple advantages to LLM, significantly improving accuracy, flexibility, scalability, personalization, and control, without needing to retrain the entire model. RAGs improve the accuracy of responses by reducing the risk of “hallucinations,” or convincing but incorrect answers, as they provide LLMs with citable and verifiable sources by the user.
This dynamic integration allows models to stay up-to-date without needing retraining, making them ideal for large-scale applications and constantly evolving contexts.
In terms of convenience, RAGs allow for the introduction of new data into an existing LLM and the elimination or updating of information sources simply by uploading new documents or files. This significantly reduces the costs and time needed to retrain or fine-tune LLMs while improving efficiency, as queries provided to LLMs are reduced to the most relevant information.
RAG offer developers greater control, allowing them to customize information sources and tailor responses to users’ specific needs. This enhancement of the user experience increases trust in the provided responses. Additionally, the RAG architecture facilitates feedback, problem resolution, and application correction, ensuring a continuous flow of updated and domain-specific information.
From a data sovereignty and privacy perspective, RAGs mitigate risks associated with using confidential information for fine-tuning LLM, keeping sensitive data on-premises. They can also limit access to sensitive information based on different authorization levels, ensuring that only authorized users can access certain data. This approach further strengthens the security and privacy of the information used.
In the next article, we will compare RAG – Retrieval-Augmented Generation with other training and data processing methods, analyzing the advantages and limitations of each approach to understand when and why to choose RAG.
