There are many advanced technologies widely used to enhance systems and software. Nowadays, another method, Retrieval-Augmented Generation (RAG), is combined with LLMS to increase efficiency. It reduces model hallucinations and combats slow updates.
RAG will basically enhance output by dynamically getting information and improving accuracy. RAG can make LLMs even more powerful. In this article, we’ll learn more about RAG and its use for Large Language Models.
So keep reading to stay updated with this latest knowledge.
What Is Retrieval-Augmented Generation?
Nowadays, Large Language Models are widely used. In this, the model first retrieves information from various documents. After doing this, the model will utilize information to improve the quality of predictions and responses of text.
With the RAG method, developers don’t need to retrain the language model repeatedly without compromising quality. RAG is well-suited for programs that deal with knowledge-intensive tasks. They consist of two states.
- Utilizing encoding models for document retrieval
- Generation phase, where the system generates text based on the retrieved context.
What Is The Difference Between RAG and fine-tuning?
There are differences between Retrieval-Augmented Generation and Fine-tuning. Here are some of the common differences that you must know.
| Aspects | RAG | Fine-tuning |
| Knowledge Update Approach | Directly updates the retrieval knowledge base, ensuring real-time information without frequent retraining, particularly effective in dynamic data environments. | Utilizes static data, necessitating periodic retraining for knowledge and data updates. |
| External Knowledge Usage | Proficient in leveraging external resources, especially well-suited for documents or other structured/unstructured databases. | Applicable for aligning externally acquired knowledge with large language models. But may present challenges in dynamic data environments. |
| Model Customization | Emphasizes information retrieval and integration of external knowledge. With limitations in fully customizing model behavior or writing style. | Permits adjustments to LLM behavior, writing style, or domain-specific knowledge, catering to specific tones or terms. |
| Ethical & Privacy Issues | Raises ethical and privacy concerns due to the storage and retrieval of text from external databases. | May evoke ethical and privacy concerns linked to sensitive content in the training data. |
| Interpretability | Facilitates traceability of answers back to specific data sources, enhancing interpretability. | Exhibits opacity, rendering it less transparent regarding why the model reacts in a certain manner. |
What Is Naive RAG?
Naive RAG is an early research paradigm that is also incorporated into chatGPT. It utilizes a framework known as “Retrieve”- “Read.” It includes indexing, retrieval, and generation. Here’s what happens in this:
- Indexing: It includes cleaning and extracting data, chunking, and embedding for creating an index.
- Retrieval: It determines all the context by calculating the similarity between the user’s query and the document blocks.
- Generation: It combines the question and documents into a prompt for the large language model to answer.
However, Naive RAG faces significant challenges in retrieval quality, especially low precision, low recall, response generation quality, augmentation process, and outdated information. Solving these issues is crucial to mitigate these
challenges.What Is Advanced RAG?
Advanced RAG has addressed some of the major limitations of Naive RAG. But how’s that possible?
It’s all because of the introduction of pre-retrieval and post-retrieval methods. The pre-retrieval process focuses on optimizing data indexing. From improving data granularity to adding metadata, mixed retrieval to alignment optimization, all of this is done in this step.
Even fine-tuning and dynamic embedding models are used to enhance relevance. Meanwhile, in the post-retrieval process, there is re-ranking and prompt compression, leading to effective content management.
RAG Pipeline Optimization
RAG Pipeline Optimization focuses on improving the efficiency and information quality of Retrieval-Augmented Generation (RAG) systems. The strategies used in this include:
- Hybrid search
- Vector search
- Combining keyword-based, semantic
- Recursive retrieval with a sophisticated query engine.
The step-back prompt is also used for reasoning regarding various concepts. It includes providing precise answers to various query strategies like subqueries. Meanwhile, the HyDE approach includes embedding similarity between the generated answers. It creates a balance between efficiency and contextually rich responses in RAG retrieval.
RAG Evaluation
Evaluating Retrieval-Augmented Generation (RAG) is a crucial aspect. It includes two primary approaches.
- Independent evaluation
- End-to-end evaluation
The retrieval module is assessed using metrics like hit rate, MRR, NDCG, and precision in an independent evaluation. Meanwhile, the generation module focuses on context relevance.
In end-to-end evaluation, the assessment is regarding the RAG model’s final response. It considers various aspects such as answer fidelity, relevance, and alignment with the input query. However, evaluation metrics may vary based on the applications of RAG. Let’s look at some of the examples.
- EM for question-answering
- UniEval and E-F1 for summarization
- BLEU for machine translation
How to Match the Semantic Space of Queries and Documents?
Two key technologies are involved in achieving alignment in the semantic space of user queries and documents in Retrieval-Augmented Generation (RAG).
Query Rewrite
The initial step is intuitive alignment. Alignment semantics is attained by rewriting queries. Large language models also play a significant role in query rewrite. LLMs guide the generation of a pseudo-document merged with the original query. Some of the examples of this approach include Query2Doc and ITERRETGEN. It’ll utilize the capabilities of large language models to create a pseudo-document.
On the other hand, HyDE establishes query vectors. It uses text indicators that generate a hypothetical document to capture relevant patterns. It introduces another framework. And it inverts the retrieval and reading order, focusing on query rewriting. LLMs generate queries with the help of web search engines.
Embedding Transformation
For fine-grained operations often embedding transformation is implemented. LlamaIndex connects an adapter after the query encoder, fine-tuning the adapter to optimize query embeddings for specific tasks.
Now comes the most crucial part. It is structured information alignment. SANTA proposes pre-training methods to ensure that retrievers are fully aware of all the structured information. It includes natural alignment for contrastive learning and Masked Entity Prediction. It prompts language models to fill in masked entities.
Various issues arise because of poor expression and lack of semantic information in user queries. With the help of these advanced technologies, these issues can be eliminated. It facilitates improving alignment in semantic space between queries and documents in RAG applications.
How To Acquire Accurate Semantic Representations?
Acquiring accurate semantic representations is extremely crucial. To achieve this, you’ve to follow two key methods. These are as follows.
Chunk Optimization
First, you need to choose optimal text chunks for correct information retrieval. But you might wonder what’s the optimal size.
The optimal size depends on various factors such as content characteristics, embedding model, user query complexity, and application needs.
Now, you need to use diverse strategies for semantic representations. Adaptive chunking strategies are used in this. Moreover, various concepts are employed in this. All of these strategies collectively contribute to enhancing retrieval efficiency and accuracy in RAG. It ensures adaptability to diverse scenarios.
Fine-tuning Embedding Models
It’s crucial to construct domain-specific datasets. These usually consist of queries, and relevant docs for fine-tuning embedding models. LlamaIndex even streamlines the process. Do you know how?
By introducing key classes and functions. With this, you can customize embedding models for specific domains. Now, it’s time to do downstream tasks fine-tuning. Thus adapting embedding models to downstream tasks, which include two paradigms.
- Domain knowledge fine-tuning caters to domain-specific information.
- Downstream task fine-tuning adapts models to tasks using the capabilities of Large Language Models.
These methods contribute to building an accurate semantic space. It ensures effective retrieval and representation in RAG.
Ecosystem of RAG
The ecosystem of RAG is diverse, and it significantly impacts downstream tasks and evaluation. RAG will focus on integrating information from a broad knowledge base. It shows us the ability to enhance LLMs even more so they can understand complex queries and fact verification.
The model will adapt to multi-domain applications in various professional domains like medicine and law. It makes evaluating RAG’s application in different downstream tasks extremely crucial.
Regarding technical aspects, key players like LangChain have rich RAG-related APIs. Many emerging technologies, such as Flowise AI, HayStack, Meltno, and Cohere Coral, bring unique characteristics. Even traditional providers like Amazon contribute to the RAG ecosystem with services like Kendra.
Ending Thoughts
Retrieval-augmented generation is a method that increases the potential of a Large Language Model. However, combining parameterized knowledge from LLMs with non-parameterized external knowledge is crucial. Despite all the progress, there’s a huge need for refinement.
There are many ways this method can lead to new and innovative technologies. Not only this, it provides a basis for more technologies.
