RAG vs Model tuning vs Large prompt window

Summary

In this conversation, Gleb Otochkin, a Google Cloud advocate, explains three key methods for integrating data into AI applications: prompts with long context windows, Retrieval Augmented Generation (RAG), and model tuning. He discusses how each method works, its strengths, limitations, and best-use scenarios. Long context windows are useful for demos and maintaining conversation history, while RAG is ideal for interactive apps needing speed and scalability with structured or multi-modal data. Model tuning provides fast, predictable responses but is suited for applications with consistent data patterns. The video provides insights into optimizing AI systems for various needs.

Takeaways

• 😀 Long context windows allow models to process large amounts of data, like entire novels, but can be slow when handling structured data, such as flight schedules.
• 😀 Models struggle with tokenizing structured data, as they are optimized for regular text, making it less efficient to process data like tables or databases.
• 😀 When working with large datasets, the response time can be slow, and caching data only reduces time for subsequent queries but doesn't eliminate the processing delay.
• 😀 Long context windows are best for demos or proof of concepts, where you need to handle a lot of conversation history or test specific features, but not ideal for production applications due to speed limitations.
• 😀 Retrieval Augmented Generation (RAG) is a faster alternative to long context windows, allowing AI models to retrieve information from databases or external sources efficiently for interactive applications.
• 😀 RAG works well with both structured data (like databases) and unstructured data (like long documents), by splitting data into chunks and searching for relevant information using embeddings.
• 😀 RAG is excellent for real-time applications that require fast responses, such as answering questions from an employee handbook or providing flight information.
• 😀 RAG is also effective for multi-modal applications, where input can include text, images, or videos, by using vector search to filter relevant content for AI models.
• 😀 Model tuning involves training a base model on specific data to enhance its performance on tasks that follow consistent patterns, like mimicking a writing style.
• 😀 Model tuning is best for applications that require fast, consistent, and predictable response times, especially when data is stable and doesn't change frequently, such as scientific papers or novels.
• 😀 While model tuning offers low-latency responses, it is resource-intensive and works best with data that exhibits clear and stable trends, making it less suited for dynamic or inconsistent datasets.

Q & A

What is the main difference between using long context windows and structured data in AI applications?

Long context windows involve adding all data into the model's prompt, which can be useful for small-scale demos or remembering previous interactions. However, for structured data, such as flight data, the model struggles to tokenize it effectively, leading to issues with performance and accuracy. Structured data is better handled using methods like Retrieval Augmented Generation (RAG) or by storing the data in a database.

What are the limitations of using long context windows for AI applications?

The limitations of long context windows include slow processing times, especially when large datasets are involved. Even though the model can fit large amounts of data into the prompt (like an entire novel), it may struggle with structured data and require significant time to process the entire dataset repeatedly, which is impractical for real-time applications.

Why is structured data difficult to handle with long context windows?

Structured data, such as tables or numerical values, doesn't tokenize well in the same way as regular text. Models are optimized for text, so they can treat each number or space in structured data as a separate token, causing inefficiencies in processing and making it harder for the model to recognize patterns.

What is Retrieval Augmented Generation (RAG), and how does it improve AI applications?

RAG is a technique that combines the retrieval of relevant data from an external source (like a database or document) with generation capabilities of a language model. It significantly improves response times, as the model doesn't need to process entire datasets for every query. Instead, it can quickly retrieve relevant information, making it ideal for interactive applications.

How does RAG handle unstructured data, such as a long text like an employee handbook?

For unstructured data, RAG works by breaking the text into smaller chunks, creating embeddings for each chunk, and then comparing these embeddings to the user's query. This allows the AI to fetch and use only the relevant sections of the text, making the response generation more efficient and contextually accurate.

When would you choose to use RAG in AI applications?

RAG is ideal for interactive applications where fast responses are necessary. It's also useful for applications dealing with large datasets, datasets that change frequently, or multi-modal inputs (e.g., text, images, videos). It is a good choice when you need real-time, contextually accurate information.

What are the benefits and challenges of model tuning in AI?

Model tuning involves training a foundational model with specific data to improve its performance. The main benefit is faster, more predictable response times for applications. However, it requires considerable time and resources for data preparation and training. It is also most effective when the training data exhibits consistent patterns, such as writing style or specific knowledge areas.

Why might model tuning not be effective for data like flight departure times?

Model tuning works best when the data has consistent patterns, such as style or format. Flight departure times, which can vary greatly and lack clear patterns, may lead to hallucinations where the model invents non-existent flights. This makes model tuning less suitable for such datasets.

What is the main difference between model tuning and using long context windows or RAG?

Model tuning involves adjusting the foundational model itself to learn from specific datasets, providing fast and consistent responses. Long context windows, on the other hand, involve adding large amounts of data directly into the model’s prompt, which can be inefficient. RAG retrieves relevant information from external sources and is more suitable for real-time interactive applications.

When should you use long context windows despite their inefficiency?

Long context windows can be useful in situations like quick demos, proofs of concept, or when the AI needs to remember a lot of past interactions in a conversation. While they are not ideal for real-time applications due to their inefficiency, they can be helpful in scenarios where speed is not as critical.