Retrieval-augmented generation (RAG) pipelines are a multi-step architecture that processes, stores, and retrieves a specific data set that is then used with large language models (LLMs) to make them more accurate and to include custom data in responses. A retrieval augmented generation system and a basic retrieval augmented generation pipeline are foundational concepts in this space, combining data retrieval with language model generation to enhance responses. Many companies find it a useful tool for keeping the data in their generative AI applications up to date and proprietary while also improving the quality of responses. Some of the more common applications that leverage RAG pipelines are chatbots, customer Q&A tools, or applications that rely on customer-specific information for success.
LLMs are revolutionary because they use massive amounts of data to create their models and can respond to many different queries. However, LLM applications can only work with the training data used to create the model. If you need to add new or custom data, you must retrain the model. This is called fine-tuning. RAG applications let you keep your already trained LLM and add new data that, as the name says, augments the retrieval of responses. A rag architecture enables the integration of company data, business data, and structured data—such as SQL tables, spreadsheets, and internal documents—into LLM workflows, improving the accuracy and relevance of generated responses.
RAG pipelines also automate prompt engineering by dynamically retrieving relevant information before generating a response. The retrieval and generation process consists of two main phases: first, retrieving data from a knowledge base, and second, generating a response using the language model based on that data.
Why Is Retrieval Augmented Generation a Popular Technology?
Although LLMs are quickly proving themselves to be a disruptive technology across industries, they are not perfect. LLM applications work by looking at a large collection of text and predicting the next most likely word in a text string or line of software code. Sometimes, the algorithm predicts wrong. Those wrong guesses, euphemistically referred to as hallucinations, occur between 2.5% and 8.5% of the time on popular LLMs. Additionally, LLMs encounter issues like creating inaccurate or irrelevant content and using outdated information, which can limit their effectiveness in dynamic environments.
Another problem with LLMs is that training them is time-consuming. When new information comes along, it doesn’t make sense to retrain or even fine-tune until you have a large amount of new data. Also, if you want to include proprietary data, say, customer information or your own internal tech support data into your model, everyone with access to the model now has a view into your data. The same is true for domain-specific data. LLMs require constant updates to provide relevant information, as their training data can become outdated, making it essential to find efficient ways to integrate new data.
On the other hand, if you train an AI system on a small set of new, domain-specific, or proprietary data, you will miss out on the massive benefits of an LLM, including natural language processing (NLP), impressive grammar, and broader context. RAG systems allow you to include new data sources for accuracy and contextualization while keeping the advantage of an LLM. In a way, a RAG pipeline automates prompt engineering.
Another appealing aspect of RAG pipelines is how the data is ingested into the system. Summarizing or translating your input from the source data is unnecessary. RAG pipelines read docs, SQL databases, websites, and can process unstructured data such as extracting information from a pdf document. The ingestion process often starts with raw data from various formats, which is then converted into a useful format. With a few lines of Python, an application can read and process data in a multitude of source formats.
In summary, RAG pipelines are popular for the following reasons:
- Information sources can be easily ingested into the data model
- You can add new information without retraining the whole LLM
- Confidential or proprietary information can be easily included in a model
- It improves the accuracy of LLMs by reducing the likelihood of hallucinations
How Does a RAG Pipeline Work?
The basic structure of a RAG pipeline consists of steps to pull data and store it, followed by retrieval operations that efficiently handle retrieved documents and retrieved data to ensure optimal performance. Then, there are steps to turn a user query—specifically, processing the input query and the user's query—into optimized and contextualized input to the LLM to improve retrieval effectiveness. RAG pipelines typically involve four steps: document preparation and chunking, vector indexing, retrieval, and prompt augmentation. Every RAG system has different steps and tools, but they all mostly fall into these stages.
1. Loading
This is the information retrieval step, also known as the data loading phase, where you load data from source documents, databases, websites, or other programs through APIs. There are hundreds of existing open source tools for data loading. Common ones include routines to load PDFs, scrape web pages, or use SQL to query data from relational databases. When working with large documents, it is important to optimize retrieval by splitting documents into smaller, manageable chunks using a document split process.
2. Indexing in a Vector Database
Once data is loaded, it is then indexed for later retrieval. In most cases, this consists of using an embedding model to convert each piece of data into a numerical representation in vector form, producing vectorized data, along with other metadata representations that make it very easy to search and find contextually relevant data. These vectors are stored as stored vectors in the database for efficient retrieval and semantic search. When processing data for indexing, it is important to consider the maximum token length of the embedding model, as lengthy documents may need to be split into smaller chunks to fit within this constraint.
3. Storing
Once indexed, the vector values and metadata are stored in a vector database, often utilizing specialized databases designed for efficient storage and retrieval of high-dimensional vector data. For small datasets, you can index in real-time with each query, but it is much more efficient to store the results of the indexing one time. It is also important to ensure that stored data in these databases is secured, especially when handling sensitive information. Organizations can configure RAG to protect sensitive customer data by storing it on-premises with self-hosted language models, ensuring compliance with privacy regulations.
4. Querying
Instead of sending a prompt directly to the LLM, a RAG pipeline takes the user question, does a semantic vector search on the vector database, and returns relevant information as part of the retrieval and generation process, which includes the generation process of the LLM. This is called retrieval. The RAG application then generates a new prompt for the LLM based on the user's question, integrating identified data and retrieved data from the vector database. This step is called generation. Hybrid retrieval strategies improve the accuracy of finding relevant information by combining keyword-based and semantic search techniques, ensuring more precise results.
5. Evaluation
Since the quality of the response returned to the user is driven by the quality of the retriever and generator in the querying step, a final step called evaluation assesses the quality of output from both the retriever and generator output. You use evaluation to calculate metrics that show how relevant, accurate, and fast your RAG implementation is.
Benefits of RAG Pipelines
RAG pipelines offer a host of compelling benefits for organizations looking to enhance the performance of large language models. By seamlessly integrating external data sources, RAG pipelines empower large language models to deliver more accurate and contextually relevant responses to user queries. This is especially valuable in fields where up-to-date information is essential, such as healthcare, finance, and legal services, where relying solely on static training data can lead to outdated or incomplete answers.
With RAG pipelines, there’s no need for frequent and expensive retraining of your large language model. Instead, you can simply update or expand your data sources, ensuring that your system always has access to the latest information. This makes RAG pipelines a scalable and cost-effective solution for building robust large language model applications.
Another key advantage is the ability to enhance domain-specific knowledge. By retrieving and grounding responses in relevant documents, RAG pipelines reduce the risk of hallucinations and provide factual, trustworthy answers. This makes them ideal for specialized applications where accuracy and reliability are paramount. Ultimately, RAG pipelines help bridge the gap between static model knowledge and the dynamic, real-world data your users care about.
RAG Pipeline Case Studies
The best way to understand where RAG pipelines are a good fit with an LLM application is to look at a few examples. The advantages of RAG pipelines drive its application towards situations where you need contextual data that is not in your LLM model. In these cases, providing context or domain knowledge is crucial for delivering accurate and relevant responses. Most applications revolve around working with a specific user, usually an internal or external customer, who needs data unique to them or some aspect of your business. The workflow relies on processed data to ensure that relevant information is retrieved efficiently. RAG can be applied in customer service chatbots, enterprise search engines, legal and financial research, healthcare, personalized education, and internal knowledge management. A question-answering workflow helps the user quickly retrieve information with minimal hallucination.
Here are a few general examples of some typical applications:
Medical Diagnostics
One area where hallucinations are dangerous and where specific data is available is Medical Diagnostics. If you prompt a general-purpose LLM like GPT-4, it will pull its response from various places, including websites that have old data or data that has not gone through a peer review. To overcome this, developers are creating RAG pipelines trained on vetted clinical data.
In one example from Singapore General Hospital, researchers built a RAG system that provided preoperative guidelines to surgeons. They used Python frameworks, including LangChain and LlamaIndex, for the ingestion of the clinical data into chunks that were then indexed in a vector store. Since clinical data often contains sensitive data, it is crucial to implement strict access controls and privacy measures during ingestion and retrieval to ensure security and compliance. Multiple LLMs were added to the pipeline, including GPT 3.5, GPT 4.0, and Llama2.
During testing against human responses to the same queries, they generated results in 15-20 seconds compared to 10 minutes for humans. The LLM-only accuracy was 80.1%. Adding a RAG pipeline bumped accuracy to 91.4%. Humans were accurate 86.3% of the time. The study concluded that with additional clinical data fed into the knowledge base, the accuracy can improve further.
FAQ-Powered Chatbot
Chatbots are the most common application for RAG systems because they can combine the power of a public LLM with customer and company-specific historical data—collectively referred to as company data—to improve chatbot accuracy. A great example is how the data scientist at Flipkart, an e-commerce company in India, explored the use of RAG pipelines to convert their frequently asked question (FAQ) capability for credit card applications into a chatbot.
Their domain-specific data consisted of 72 FAQs, and they used a version of GPT 3.5 for their LLM. After some experimentation, this new system’s accuracy was 100%. In addition, they used the RAG pipeline to discover out-of-context prompts and skip using the LLM. Since they use a commercial LLM, they saved on the cost of tokens along with more accurate responses for their customers. They could answer user questions directly instead of making the users read an FAQ while reducing their spending with their LLM provider.
E-Commerce Site Search
Searching on an e-commerce site is another common way to use RAG pipelines. A RAG pipeline can ingest customer-specific data—also known as business data—used to personalize search results in real-time. Standard search engines use an algorithm that uses keywords and the popularity of a given piece of data. LLMs go further to find the most probable answer. Adding a RAG pipeline optimizes the search process, giving personalized and relevant results that increase click-through rates and conversions to sales.
Custom Data: Tailoring RAG to Your Domain
One of the greatest strengths of a RAG pipeline is its flexibility to work with your own data. By loading and indexing domain-specific data—whether it’s PDF documents, CSV files, or other sources of relevant information—you can ensure your RAG pipeline is finely tuned to your unique business needs. This customization allows your system to deliver highly accurate and relevant responses to user queries, even when those queries require specialized knowledge or terminology. RAG allows for domain-specific knowledge integration, enabling the creation of custom AI assistants for specialized tasks.
For example, a company can import documents containing internal policies, technical manuals, or customer support logs, and use this custom data to power their RAG pipeline. This approach ensures that when users ask questions, the system retrieves and references the most relevant information from your own data, rather than relying solely on generic sources. The result is a RAG pipeline that not only understands your domain but also provides responses that are tailored to your users’ specific needs. RAG enables organizations to create knowledge engines that help employees get answers to questions related to HR or compliance documents.
By leveraging custom data, you can build a RAG pipeline that stands out in its ability to answer user questions with precision and authority, making it an invaluable tool for any organization with specialized requirements.
Prompt Engineering for RAG Pipelines
Prompt engineering plays a pivotal role in the effectiveness of RAG pipelines. The way you structure prompts directly influences how the large language model interprets user queries and generates responses. Well-crafted prompts provide the necessary context and guidance, enabling the model to deliver more accurate and relevant responses.
In practice, prompt engineering involves designing clear, concise, and purposeful prompts that help the large language model understand the intent behind user queries. This might mean including retrieved context from your RAG pipeline, specifying the type of answer required, or tailoring prompts for specific tasks like summarization or question-answering. For domain-specific applications, prompt engineering can also involve incorporating terminology or instructions unique to your field.
By investing in prompt engineering, you can significantly boost the performance of your RAG pipeline, ensuring that user queries are met with responses that are not only relevant but also actionable and trustworthy.
RAG Pipeline Implementation Challenges
Although RAG pipeline implementation is fairly simple, it does have its challenges. Here are the most common obstacles people run into:
- Picking the right algorithms and strategies: RAG pipelines are an architecture, not a set collection of tools. At every step in the pipeline, you will need to pick the appropriate algorithms. The sheer number of choices can be overwhelming.
- Managing a multitude of AI components: Once you have chosen the algorithms and strategies you want to use, you need to then select AI components and make sure they work together. It is easy for things to get complicated and for connections to fail, and finding bugs can be difficult. Effective API key management is also crucial for secure and efficient integration with external models.
- Getting data extraction right: The online step-by-step tutorials often show a Python script that points to a directory and reads a pile of PDF files. You may get lucky and your extraction could be that simple, but odds are you will have multiple data sources, APIs to other programs, and quality issues with the data. Figuring out extraction can take time if you are not working with a team that has done it before.
- Managing data: The heart of any RAG pipeline is the data, and any implementation will face challenges around skipping out-of-context data, cleaning out old information, handling processed data to ensure data quality, and adding new data. RAG systems can lead to potential biases, highlighting the importance of rigorous data curation.
- Scaling software and hardware: Every step in a RAG pipeline uses computational and storage resources, both during ingestion and when responding to prompts. If your system doesn’t scale, users may experience unacceptable delays.
Best Practices for Managing and Optimizing Your RAG Pipelines
At Focused Labs, we have helped many customers implement generative AI technology across industries and application platforms, including many projects that took advantage of RAG pipelines. Here are a few of the lessons we have learned:
- Make regular updates to data and technology: Your responses are only as good as your data. Put processes in place to evaluate and update that data. The technology in the pipeline also evolves, so put steps in place to keep each module up to date and replace modules when appropriate with newer, better technology. It's especially important to ensure your deployed language model and its integrations are regularly updated to maintain seamless data extraction and access to current information. We recently did this with an in-house RAG application, upgrading to the LangChain Expression Language (LCEL), and saw great results.
- Diversify data sources: Just as with an LLM, the quality of the responses retrieved from your vector database increases with the diversity of data sources. Adding new collections can help weight responses as well as increase accuracy.
- Experiment and evaluate: The first pass at a RAG pipeline is just the start. Build evaluation and experimentation into your development and maintenance processes. Play with parameters, data sources, component technology, and optimize retrieval operations to improve the speed and quality of your pipeline.
- Work with experts: This area of AI is a bit of a specialty. There are a multitude of online training resources and an overwhelming amount of open source tools. But none of those replace spending time with a team who has implemented the technology before, knows all the terminology, and, most importantly, understands the theory behind not just RAG pipelines, but LLMs, API implementation, and fundamental software development methods. Working with a partner like Focused Labs can be a game changer.
- Use good DevOps practices: Although unique in many ways, a RAG pipeline’s development and deployment are still software projects, and good DevOps practices can make an implementation go much smoother.
Future of RAG Technology
The future of retrieval augmented generation is bright and full of innovation. As large language models continue to advance, the demand for RAG pipelines that can deliver precise, up-to-date, and contextually relevant responses to user queries will only increase. We’re already seeing rapid progress in areas like semantic search, which allows RAG pipelines to better understand the meaning behind user queries and retrieve the most pertinent information.
Advancements in prompt engineering and retrieval augmented generation will further enhance the ability of RAG pipelines to provide relevant responses, even for complex or highly specialized tasks. Additionally, as RAG pipelines become more integrated with other AI technologies—such as computer vision and advanced natural language processing—their potential applications will expand into new domains, from customer service to knowledge management and decision support.
Looking ahead, regular vector database updates, smarter data indexing retrieval, and more sophisticated embedding models will make RAG pipelines even more powerful and adaptable. As organizations continue to seek ways to leverage large language models for real-world challenges, RAG pipelines will remain at the forefront of delivering accurate, reliable, and contextually aware solutions.
Getting Started With Implementing a RAG Pipeline
For the right application, we have found RAG pipelines to be an efficient and flexible solution to improve accuracy and easily incorporate specific data into generative AI applications. Once you have identified an application that will benefit from this technology, we recommend the following steps:
- Pick your LLM
- Identify your data sources
- Choose your pipeline components, including selecting appropriate vector databases for efficient data retrieval and the right embedding model
- Build an MVP
- Test, debug, and improve
- Build out the full application
- Deploy
- Establish a maintenance and updating process
You have the contextual data, you have the application, and now you just need to build it. And our team at Focused Labs is eager to help. We are a collection of experts who love their craft, know how to listen and collaborate, and are obsessed with asking questions and learning continuously. We don’t just show up, drop off code, and leave. We design and build solutions as a partner with our customers and also train them as we go so when we are finished, you can continue the work. Reach out and let’s build something together!
Conclusion
In summary, RAG pipelines represent a transformative approach to enhancing large language model applications. By integrating diverse data sources and leveraging prompt engineering, RAG pipelines enable large language models to provide accurate, relevant responses to user queries—grounded in real, up-to-date information. This not only reduces hallucinations but also makes RAG pipelines a scalable and cost-effective solution for a wide range of use cases.
Whether you’re working in a specialized domain or simply want to improve the quality of your large language model’s responses, RAG pipelines offer a flexible and powerful way to meet your goals. By tailoring your RAG pipeline to your specific needs, investing in prompt engineering, and staying current with the latest advances in retrieval augmented generation, you can unlock the full potential of this technology and deliver high-quality, relevant responses to your users.
RAG pipelines are poised to revolutionize the way we interact with large language models, making them smarter, more reliable, and better equipped to handle the ever-evolving demands of real-world user queries.