How to chat with your own data?

Discover RAG to chat with your personal data

Understanding RAG

• Relevance Augmented Generation (RAG) is a recent method in artificial intelligence that combines information retrieval and content creation. Usually, large-scale language models (LLMs) produce content based only on the information they acquired during their training. However, RAG allows the model to "consult" a database or set of external documents in real time to enrich the text it generates. The RAG process can be broken down into two main steps:
• Retrieval: when the model receives a request, it searches a predefined set of documents or data to find the most relevant information related to the request.
• Generation: after retrieving the relevant information, the model uses it, in addition to its own internal knowledge, to generate a response or content that not only addresses the initial request, but does so in a more informed and precise manner.
• Integrating RAG into generative AI systems offers many significant advantages, improving not only the quality of the generated content, but also its applicability in various contexts. By relying on an external database to complement its knowledge, a RAG model can provide more precise and relevant answers to questions.

How can RAG improve human-machine interaction?

With Relevance Augmented Generation (RAG), we are entering a new era of human-machine interactions that are more precise, dynamic, and enriching than ever before! Thanks to RAG, the precision of the responses generated by AI systems reaches unprecedented heights. These responses are not only accurate, but they are also enriched by a deep context. Thus, users benefit from tailor-made solutions that perfectly meet their specific needs. A truly enhanced user experience! RAG also enables these AI models to generate dynamic and constantly updated content. Unlike traditional approaches, RAG draws from a vast array of knowledge to provide responses enriched by continuously relevant data. Imagine the engagement and added value this brings to human-machine interactions! And the benefits don't stop there. RAG has a remarkable positive impact on the user experience of AI applications. Thanks to more precise and relevant responses, users are naturally more satisfied and inclined to use these systems regularly. A crucial asset in a world where user experience has become a key factor for success. But the versatility of RAG doesn't end there. This technology can be deployed in a multitude of applications, from question-answering systems to content generation, offering potential for improving human-machine interaction in many different contexts. To top it off, RAG-based tools also offer an interactive and engaging learning method. Users can interact with these systems as if they were talking to a human being. A true revolution in AI-assisted learning.

What is the impact of RAG on the field of AI?

Relevance Augmented Generation (RAG) has a significant influence on the field of Artificial Intelligence:

• Optimization of relevance and accuracy: By leveraging an external database to enrich its knowledge, a RAG model is capable of providing more precise and appropriate answers to questions.
• Integration of internal and external resources: RAG offers the ability to connect generative AI services to external resources, particularly those containing the most recent technical information.
• Building user trust: Relevance augmented generation allows models to cite sources, giving users the ability to verify all statements, thereby strengthening their trust.
• Reduction of errors: This technique can help models eliminate any ambiguity in a user's request and minimize the risk of model error.
• Simplicity of implementation: Another major advantage of the RAG method is its simplicity of implementation. Developers can implement the process with just a few lines of code.

RAG thus represents a major step towards creating a safer and more responsible AI. By reducing errors and providing verifiable information, it sets a new standard for the use of AI in sensitive applications.

Create your bot with RAG on Swiftask

What types of data can RAG use for information retrieval?

Relevance Augmented Generation (RAG) can use different types of data for information retrieval, including:

• Raw text: documents, articles, blogs, books, etc. can be used as sources of information for retrieval.
• Structured data: databases, tables, and charts can also be used for information retrieval.
• Unstructured data: images, videos, and audio recordings can be transformed into text through natural language processing (NLP) techniques and used for information retrieval.
• Multilingual data: RAG can use data in different languages for information retrieval.

It is important to note that the quality of the data used for information retrieval can have a significant impact on the performance of RAG. Therefore, it is essential to have accurate, relevant, and high-quality data to achieve the best results.

Factors to consider when using RAG for content generation

When using Relevance Augmented Generation (RAG) for content generation, several factors should be taken into consideration:

• Data quality: The quality of the data used for information retrieval is crucial. Accurate and relevant data can improve RAG's performance.
• Data diversity: Data diversity can help generate richer and more varied content. Therefore, it is beneficial to use a wide range of data sources.
• Model parameters: Model parameters, such as the number of documents to retrieve or the weight given to information retrieval, can have a significant impact on the results.
• Context understanding: RAG must be able to understand the context in which it is used. This includes understanding the topic, the target audience, and the goals of content generation.
• Ethics and bias: It is important to monitor and minimize potential biases in content generation. Additionally, the generated content must adhere to ethical and legal standards.
• Performance evaluation: It is essential to regularly evaluate RAG's performance to ensure that it meets expectations and to identify areas for improvement.

These factors can help optimize the use of RAG for content generation.

Creation of the bot with RAG on Swiftask: practical test

Let's create an intelligent bot that will interact with us based exclusively on the data we provide.

1. On Swiftask, start by inserting the necessary document. To do this, go to "my data" and import the desired file type.

2. Next, focus on creating the bot. Just above "my data," click on "my bots."

3. Give a name to your bot and choose the RAG, as you will provide it with data that only you know.

4. After clicking on "Continue," select the AI that will serve as the engine for your bot. You can choose from the following options: LLAMA 2, GPT-3.5, Mistral Large, Mistral Medium, and Mistral Small.

5. Regarding the prompt, Swiftask has already inserted a default text. If needed, modify it or add text as per your requirements.

6. Once the prompt is finalized, insert the required data. To do this, go to the top of the page and click on "Data Sources" to integrate the data you previously imported.

7. Once this step is completed, click on "Save" and test your new bot. Adjust the prompt if the responses do not suit you.

For developers, a dedicated page is also available. It allows you to integrate your bot into your website or application.