Cosa sono i RAG, spiegato semplice (retrieval augmented generation)

Summary

This video discusses common challenges faced by AI tools like hallucinations, outdated information, and limited context windows. The speaker introduces RAG (Retrieval Augmented Generation) as a solution for businesses, especially in specialized fields like law and medicine. RAG uses vector databases to retrieve reliable, updated information from custom archives, enhancing the quality of AI responses. The video explains the three steps of RAG—retrieval, augmentation, and generation—making it clear how this framework improves AI accuracy. The speaker also emphasizes how tools like GPT can be tailored for specific use cases by integrating custom documents.

Takeaways

• 😀 LLMs like ChatGPT, Copilot, and Gemini experience 'hallucinations,' where they generate incorrect or fabricated information.
• 📅 These tools have outdated information because they are trained on data that cuts off at a certain point, such as December 2023 or January 2024.
• 🤖 LLMs struggle with specialization and are more generalists, performing well for broad tasks but less so for niche, industry-specific needs.
• 💡 The 'context window' is limited in LLMs, meaning there's a restriction on the amount of input and output they can handle at once, with GPT having one of the smallest windows.
• 🔎 RAG (Retrieval Augmented Generation) is a method that combines LLMs with a reliable, up-to-date database to improve accuracy and relevance.
• 📚 Vector databases are used in RAG for storing data in multidimensional vectors, which allows for faster, more semantically aware retrieval of information.
• ⚖️ RAG is especially useful in fields where accuracy is critical, like legal and medical industries, as it allows AI to retrieve and use verified, reliable data.
• 🏛️ A use case for RAG includes law firms storing case histories and legal codes in a database, allowing an AI to quickly retrieve accurate information.
• 🧠 The RAG process involves three phases: retrieval, augmentation (adding context from the database), and generation (LLM creates a response based on this data).
• 🏥 Another example of RAG's potential is in healthcare, where AI can access updated medical procedures, clinical records, and scientific papers to support decision-making.

Q & A

What are hallucinations in the context of AI models?

Hallucinations refer to errors made by AI models, such as providing incorrect or fabricated information.

Why do AI models have outdated information?

AI models are trained on datasets that are fixed at a certain date, meaning they do not include information or events that occur after that date.

What is the limitation of AI models regarding specialization?

AI models are generally designed to be generalists, which means they may not provide satisfactory answers for highly specialized or niche industries.

What is a context window in AI models?

The context window refers to the limit on the number of words or characters that can be inputted or outputted by the AI model in a single interaction.

What is Retrieval Augmented Generation (RAG)?

RAG is a framework that enhances AI responses by integrating a reliable, updated database from which the AI can retrieve accurate information.

How does RAG improve the reliability of AI responses?

RAG allows the AI to pull information from a curated database, ensuring that the responses are based on authoritative and current data.

What are vector databases, and how are they related to RAG?

Vector databases are advanced storage systems that organize information in multidimensional vectors, allowing for efficient retrieval based on semantic similarity, which is essential for RAG.

Can you provide an example of RAG in a legal context?

In a law firm, RAG can be used to create a chatbot that accesses a database of past cases and legal documents to provide accurate information for ongoing cases.

What are the three phases of RAG?

The three phases of RAG are retrieval (accessing the database), augmentation (adding retrieved information to the initial query), and generation (creating a coherent response).

Why is it important to have updated information in fields like medicine or law?

In critical fields like medicine and law, providing accurate and up-to-date information is essential, as incorrect information can lead to serious consequences.