Retrieval Augmented Generation:
Best Practices and Use Cases

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Introduction

Statistics reveal that tools like ChatGPT, which are part of generative AI, can automate up to 60-70% of tasks that typically consume employees’ time. However, 56% of business leaders are hesitant to adopt these tools, citing concerns over bias and inaccuracies in AI-generated content.

Retrieval augmented generation (RAG) stands as a groundbreaking approach, merging the capabilities of active retrieval systems with advanced generative models. This technique, especially in the field of multimodal language modeling, offers a synergy of precision and creativity, empowering applications across various fields.

  • Access to current and reliable facts: The AI can utilize the latest and most accurate information.
  • Transparency in information sources: Users can see where the AI gets its data, ensuring trustworthiness, which naturally leads us to consider the risks associated with biased information in LLMs.

Despite its potential, RAG is relatively new and not widely known among business leaders.

Path of development

In 2020, a team at Meta Research created something called RAG models to deal with information more accurately. These models, as explained by Lewis and his team, are a new way to improve how computers understand and use information. They combine two types of memory systems: one is like a computer’s long-term memory that knows a lot of language (called parametric memory), and the other is more like a searchable database, which, in this case, is a collection of Wikipedia articles (non-parametric memory).

Lewis and his colleagues worked on making these RAG models better. These models mix the language knowledge from the first type of memory with the Wikipedia database. They use a special tool (a pre-trained neural retriever) to search Wikipedia. There are two kinds of RAG models: one uses the same Wikipedia articles for the whole text it creates, and the other can switch between different articles for different parts of the text.

They improved these models by training them on tasks that need a lot of knowledge. The exciting part is that these models set new records in answering open-ended questions. They were better than the older models that just used language memory or those that combined searching and extracting information. When it came to creating text, these RAG models were more accurate, diverse, and true to facts than the best models before them that only used the language memory system.

The way RAG systems work

Active RAG systems dynamically retrieve information from external sources, such as databases, the internet, or specific document sets, in real time. This process is not just a passive retrieval of information but an active, contextual search based on the user’s query or the conversation’s context. The system then uses this retrieved data to inform and shape the responses it generates. The key components of active RAG are:

  • Dynamic information retrieval Active RAG systems continuously search for and update their external knowledge sources, ensuring the information they use is current and relevant.
  • Context-aware processing The system understands and analyzes the context of a query, enabling it to retrieve information that is precisely relevant to the user’s needs.
  • Integration with LLMs The retrieved information is seamlessly integrated into the generative process of LLMs, ensuring that the responses are not only accurate but also naturally phrased.

RAG, in general, operates in two phases:

  • Retrieval phase: Algorithms search for and retrieve relevant information based on the user’s query. This can include real-time data, user-specific details, and updated factual content.
  • Content generation phase: After retrieval, a generative language model like GPT uses this context to generate responses. The responses are conditioned on the retrieved data for accuracy and may reference the information sources.
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Retrieval Augmented Generation:
Best Practices and Use Cases

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Best practices in RAG implementation

When using RAG in a machine-learning platform, it’s important to remember several key best practices:

  • Data quality and relevance: The effectiveness of RAG heavily depends on the quality of the data it retrieves. Ensuring the relevance and accuracy of the data in the knowledge base is crucial. For instance, if you’re creating a RAG system for a medical advice chatbot, your data sources should include verified medical journals and publications. Using outdated or non-peer-reviewed sources could lead to inaccurate medical advice.
  • Fine-tuning for contextual understanding: It’s important to fine-tune the generative models to understand and utilize the context provided by the retrieved data effectively. For example, in a travel recommendation system, the RAG should not only retrieve data about destinations but also understand the context of queries, such as “family-friendly places in Europe in winter.” This ensures the suggestions are contextually appropriate.
  • Balancing retrieval and generation: Achieving a balance between the retrieved information and the creative input of the generative model is key to maintaining the originality and value of the output. To visualize, in a news summarization tool, the RAG system should retrieve accurate news details but also generate summaries that are concise and retain the news’ essence without over-relying on the retrieved text.
  • Ethical considerations and bias mitigation: Given the reliance on external data sources, it’s essential to consider ethical implications and actively work to mitigate biases in the retrieved data. If your RAG system is used for resume screening, ensure that the data it learns from does not contain biases against certain demographics. Regularly update the data pool to represent a diverse range of candidates.

Use cases

Some key use cases where RAG is particularly effective and should be highlighted:

  • Customer support chatbots: In customer service, RAG can empower chatbots to give more accurate and contextually appropriate responses. By accessing up-to-date product information or customer data, these chatbots can provide better assistance, improving customer satisfaction. Ada, Amelia, and Rasa are real-world chatbots utilizing RAG, used by companies like Shopify, Bank of America, and Salesforce, to answer customer queries, resolve issues, complete tasks, and collect feedback.
  • Business intelligence and analysis: Businesses can use RAG to generate market analysis reports or insights. By retrieving and incorporating the latest market data and trends, RAG can offer more accurate and actionable business intelligence, as utilized by platforms like IBM Watson Assistant, Google Cloud Dialogflow, Microsoft Azure Bot Service, and Rasa.
  • Healthcare information systems: In healthcare, RAG can improve systems that provide medical information or advice. By accessing the latest medical research and guidelines, such systems can offer more accurate and safe medical recommendations. HealthTap and BuoyHealth are healthcare chatbots using RAG to provide patients with health condition information, medication advice, doctor and hospital finding services, appointment scheduling, and prescription refills.
  • Legal research: Legal professionals can use RAG to quickly pull relevant case laws, statutes, or legal writings, streamlining the research process and ensuring more comprehensive legal analysis. Lex Machina and Casetext are real-world legal research chatbots using RAG to assist lawyers in finding case law, statutes, and regulations from various sources like Westlaw, LexisNexis, and Bloomberg Law, providing summaries, answering legal queries, and identifying potential legal issues.
  • Content creation: In content creation, like writing articles or reports, RAG can improve the quality and relevance of the output. It does this by pulling in accurate, current information from various sources, thereby enriching the content with factual details. Jasper and ShortlyAI are examples of real-world tools that use RAG for creating content.
  • Educational tools: RAG can be used in educational platforms to provide students with detailed explanations and contextually relevant examples, drawing from a vast range of educational materials. Notably, Duolingo uses RAG for personalized language instruction and feedback, while Quizlet employs it to generate tailored practice questions and provide user-specific feedback.

RAG in multimodal language modeling

Building on the diverse applications of RAG models in fields like customer support, business analysis, healthcare, legal research, content creation, and education, we now turn our attention to an emerging area of exploration. Here, we investigate how RAG can be integrated with different types of data, like images and videos, to further improve its capabilities in language understanding and generation.

Recent advances in models like DALL-E and RA-CM3 have led to significant achievements in converting text to images and vice versa. These models store all their knowledge (like how the Eiffel Tower looks) within their parameters, leading to the need for bigger models and more data to hold more knowledge. To address this, Yasunaga and the team developed a new model, Retrieval-Augmented CM3 (RA-CM3), which uses a different approach. RA-CM3 has a base multimodal model (the generator) that can refer to related text and images brought in by a retriever from external sources, like web documents. RA-CM3 represents a unique and the first multimodal model capable of retrieving and generating both text and images. In comparison to baseline models like DALL-E and CM3, RA-CM3 shows a significant improvement in both image and caption creation tasks. Additionally, RA-CM3 has new abilities, such as creating accurate images and learning in a multimodal context, like generating images based on demonstrations. As Yasunaga stated, RA-CM3 is capable of accurately producing images featuring either uncommon subjects (such as a “Ming Dynasty vase”) or compositions with multiple subjects (like the “Statue of Liberty” alongside the “Washington Monument”).

The integration of RAG in multimodal language modeling allows models to not only understand and generate text but also to interpret and incorporate information from other modalities like images or sounds. This broadens the scope of applications, making AI systems more versatile and capable of handling complex tasks that require understanding beyond just text.

LangChain and LLM RAG

Following our exploration of how RAG models can be integrated into multimodal language modeling, enabling AI to comprehend and utilize diverse data types like images and sounds, we now shift focus to LangChain. Here, we delve into the fusion of LangChain technology with RAG models within LLMs, uncovering how this combination improves the AI’s ability to process, understand, and generate more sophisticated and contextually rich language outputs.

By leveraging RAG, LLM platforms can provide more accurate, contextually rich, and detailed responses, significantly improving user experience and the quality of output in various applications. It represents an open-source developer framework designed for developers to create applications using LLMs. It is especially beneficial for querying specific documents like PDFs and videos or for interacting with personalized data through chat.

Langchain stands out as the preferred Python library for streamlining LLM workflows. Its popularity can be attributed to its user-friendly abstraction. Similar to Python, which is preferred in AI for its simplicity and rich ecosystem, Langchain simplifies the use of language models. It combines ‘Language’ and ‘Chain’ in its name, reflecting its ability to draw out reasoning from language models. It provides numerous built-in features and utilities that ease development tasks. By offering a high-level interface for working with LLMs, it also allows developers to create applications quickly without delving into complex underlying details. It features a modular and straightforward design, filled with useful functions for building LLM-powered applications.

Here are some applications of RAG and Langchain that should be highlighted:

  • In generative search, representing a new search framework that uses LLMs and RAG to change how we interact with search engines. Tools like Bing Chat and You.com utilize RAG to power their search capabilities.
  • In chatting with data, where recent startups have developed products that let users interact with their documents conversationally. RAG transforms static content into interactive knowledge sources, simplifying information retrieval.
  • In using the customer service chatbots, where RAG, in the next generation of chatbots, can provide accurate, personalized, and context-aware assistance. These bots can access a knowledge base, improving customer service and fostering brand loyalty.

An example of SPARK- Prompt Assistant, demonstrates the effective combination of Langchain and RAG in creating smart assistants. These assistants enable natural, engaging, and useful AI interactions. SPARK is adept at providing precise and insightful responses to inquiries about prompt crafting. Additionally, it serves as a helpful resource for understanding the basics of prompt design and engineering.

Conclusion

As the technology develops, we can anticipate RAG becoming more advanced and versatile. A notable future direction for RAG is the inclusion of multimodal abilities, enabling it to handle not just text but also images, videos, and audio. Furthermore, RAG could be utilized to access a variety of APIs, improving LLMs with multi-faceted capabilities and providing users with an enriched experience. For example, RAG can use APIs to gather real-time data, such as weather updates, public holidays, flight schedules, and tourist attraction information. This feature could be particularly useful for a user planning a vacation, as the RAG-improved LLM could offer a comprehensive travel guide, bringing together a wealth of relevant, current information without needing human input.

In conclusion, RAG marks a significant step forward in the field of AI, offering both accuracy and creativity that has enormous potential. By understanding and implementing best practices and exploring its diverse use cases, we can harness the full potential of RAG in various domains. As we continue to explore and refine this technology, the possibilities for its application seem boundless, promising a future where AI is more helpful, accurate, and insightful.

Deepchecks For LLM VALIDATION

Retrieval Augmented Generation:
Best Practices and Use Cases

  • Reduce Risk
  • Simplify Compliance
  • Gain Visibility
  • Version Comparison
TRY LLM VALIDATION

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