Retrieval Augmented Generation (RAG): Explained

Thanks to large language models, software applications now work in a new and improved way. One major drawback of these models is that they can’t update or adapt unless they’re retrained. Which limits their flexibility. Creating custom AI applications with your data by training a large language model is often not feasible for businesses. This is because of various factors like limited resources and the closed-source nature of the most widely used LLMs.

Retrieval Augmented Generation (RAG) has become the most widely used framework to address this problem as of 2024. It makes it simple and reasonably priced for developers and businesses to create personalized apps using their data on top of LLMs.

This blog discusses RAG’s definition, operation, and significance. What you should know is as follows:

What is Retrieval Augmented Generation?

Retrieval Augmentation Generation (RAG) is a setup that improves a Large Language Model. It does this by combining it with an information retrieval system and a separate knowledge base. The retrieval system works in real time by searching the knowledge base. It sits above the LLM and adds more information to improve its responses.

The knowledge base is curated separately. It might have private or time-sensitive data that can improve LLM applications.

Every day, we witness the application of retrieval augmented generation (RAG) in:

• ChatGPT web-search
• Internal enterprise chatbots
• “Chat to PDF” services

How Does Retrieval Augmented Generation Work?

A query that is sent to an LLM-based RAG system does not go directly to the LLM. The query goes through an information retrieval component first. It then gets extra information from a specific knowledge base. The original query and the information that was retrieved are then sent to the LLM in a prompt template. The LLM’s next task is to analyze the information that has been retrieved and, using the user’s query as a guide, provide a pertinent answer.

Build a Knowledge Base

An external database is needed to use RAG. This database should typically include information that hasn’t been included in the LLM’s training set. For instance, a business might use its data from private databases or documents in a company chatbot.

The most common way to build a knowledge base is by gathering useful, clear text data. Then, it’s stored in a vector database using an embedding model. Here, the text data becomes numbers (vectors) using the embedding model. It does this by understanding the meaning of the text. After that, this is kept on a vector database like ChromaDB or Pinecone.

Information Retrieval

The retrieval component bases its search on the query entered by the user. For example, if someone asks “Who is the UK Prime Minister,” and you want to search using Google keywords. It will search and show the top results. With the aid of tools, Humanloop can easily appliance this.

Semantic search inside a vector database is a popular method. This process includes converting the knowledge base into a vector format. Which represents the meaning of the data using numerical values. When a user asks a question, the system immediately changes the words into numbers. Then, it checks how similar these numbers are to what’s already saved in the database using special algorithms. The knowledge base is then queried to extract the vectors (data points) that, about semantic meaning, are most alike the query vector.

It is critical to get this process operating efficiently since it will affect the RAG system’s dependability. In this case, you can try different ways of setting up the database and finding information. For more details, our guide on improving LLM apps has useful information about this.

Augment the Prompt Template

The guidelines you give the LLM serve as the prompt template. Here, you provide a step-by-step description of the task and enable dynamic user query and chunk retrieval inputs. The LLM will observe something that looks like this:

The LLM then produces a response based on this. The most important part of the RAG framework is the prompt template. So, it’s crucial that the model understands its task well and does it consistently.

For your model to know when (and when not) to use the retrieved data, you must prompt engineer it correctly at this point. Using a bigger model like GPT-4 or Claude 3 with improved reasoning skills will lead to much better performance. So, it’s recommended to opt for these models.

Evaluate performance

It’s crucial to test and estimate the RAG system carefully during each development phase. This is especially important before putting it into use. At every stage of development, Humanloop’s assessment features support this process.

You can use Humanloop to create test sets of inputs. It can automatically estimate tools and prompts, like your retriever. The performance is scored based on the metrics you choose. You can create evaluators using AI, Python code, or human review. The choice depends on how complex the judgment required for evaluation scores is. Read about our method for assessing LLM applications to find out more.

Why is Retrieval Augmented Generation Important?

The most widely used technique for giving LLMs more context without requiring them to undergo retraining is RAG. It’s an easy-to-use and reasonably priced method. It offers a lot of flexibility and plenty of chances for experimentation.

Since powerful foundational models like GPT-4 are owned by companies, RAG becomes crucial. It helps adjust their functionality for specific uses. It can also be used to resolve common issues with LLMs, like disinformation or hallucinations.

Benefits of Retrieval Augmented Generation

Speed

Developers can test and iterate on performance quickly with RAG frameworks because they can be built rather quickly. As a result, teams can quickly appliance AI in production and save time to value.

Cost Effective

RAG is a far less expensive option when compared to training an LLM for an enterprise application. The price depends on how many tokens you use in each API call to the models. Setting up costs are low, and there’s room to experiment.

Information is Current

Famous LLMs like Claude 3 and GPT-4 stay the same even after training. Which typically happens a few months after they’re launched. RAG can get around this by including up-to-date data in the knowledge base.

Increased User Trust

Within the RAG framework, developers can organize the knowledge base. This ensures that any retrieved information comes with a source reference. Users can check the source to make sure the information is correct. This makes it easier to see. This is helpful in RAG applications for legal and financial settings and is referred to as “grounding the model.”.

Retrieval Augmented Generation with Humanloop

A teamwork platform called Humanloop helps make and test LLM apps. We handle crucial tasks like evaluating models and designing prompts. This allows product managers and engineers to work together on identifying. Additionally, there are tools to closely track and estimate performance. These tools use both automated tests and feedback from users.

You can harness Humanloop to build different components of your RAG system. This involves testing, evaluating, and monitoring performance in both prompt and tool (retriever) setups. Your teams will find it simpler to collaborate as a result and move AI from the playground to production.

Conclusion

Retrieval Augmented Generation (RAG) is a groundbreaking method in natural language processing. It combines retrieval-based and generative models, transforming how we approach language tasks. RAG integrates information retrieval with text generation smoothly. It helps AI systems produce relevant, coherent, and insightful responses more effectively.

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