Google's big move! Is RAG technology dead?

Google recently releasedGemini 2.0 FlashThis version model may be the most cost-effective AI model currently.

In addition to cost-effectiveness, what else is this model good for? Why do I sayRAGAbout to be eliminated?

What exactly is RAG?

RAGThe full name isRetrieval-Augmented Generation, which stands for retrieval-augmented generation technology . This technology is often used to help AI models such as ChatGPT access external information beyond their original training data.

You may have experienced it without realizing it.PerplexityOr other AI searches?

When they answer questions and search for information, that's RAG at work.

Even when you upload files to ChatGPT and ask questions, RAG technology is also used.

RAG is important because early AI models had extremely limited memory capacity .

Back in early 2023, mainstream models could only process about 4,000 tokens (equivalent to 6 pages of text).

This means that when faced with massive amounts of information, complex operations such as block cutting and vectorized storage (embedded technology/vector database/block processing, etc.) must be performed.

Then retrieve related fragments as needed.

But now?

This process can probably be consigned to history.

Gemini 2.0 Flash is here

While all current AI models can process large amounts of information, what’s special about Gemini 2.0?

It can process 1 million tokens at a time .

Some models even reach 2 million tokens .

This means that you no longer need to split the data into fragments, but can feed the complete document directly to the model and let it reason as a whole .

More importantly, the new generation of models not only has larger memory capacity , but also significantly improved accuracy .

Google's latest model has a record low hallucination rate (the probability of making up something).

This alone brings about a qualitative leap.

The power of paradigm shift

Here’s a real-world example: suppose you have an earnings call transcript that’s 50,000 tokens long (which is pretty big).

If you use the traditional RAG solution, you need to cut it into small blocks of 512 tokens for storage.

When a user asks a question, the system needs to retrieve relevant snippets and then input them into the model.

The problem is: the model cannot perform global reasoning .

For example, when a user asks:

"How does the company's revenue this year compare to last year?"

If only scattered blocks of text are provided, the answer will inevitably be inaccurate.

But what if the complete record is entered into Gemini 2.0?

It can provide a comprehensive and accurate analysis of the situation, from the CEO's opening remarks to the core data and then to the analyst Q&A session.

So when I say RAG is dead , what I really mean is:

Traditional RAG methodology (cutting a single document into pieces) is outdated .

You no longer need this cumbersome process.

Just hand over the complete document to the big model.

But RAG is not completely dead.

Some people suggested:

"What if there are 100,000 documents?"

Good question!

Faced with extremely large-scale data sets - such as all of Apple's financial reports for the past decade, this still requires a screening mechanism.

But the methodology has been innovated, my new solution is:

1. Retrieve relevant documents first (e.g. extract only Apple financial reports from 2020 to 2024)
2. Feed the full document into the AI ​​model in parallel
3. Integrate the output of each document to draw the final conclusion

Compared with the traditional block method, this solution is more accurate.

Let AI reason at the level of complete documents rather than processing fragmented data.

The following figure shows the process of modern solutions to handle massive documents

Core revelation

If you are developing an AI product or conducting an experiment, remember that simplicity is the best way to achieve success .

Most people fall into the trap of over-designing.

Upload complete documents directly to Gemini 2.0 (or any large context window AI model) and let the model reason autonomously .

Will the technology evolve again next year? Very likely.

AI models are getting cheaper, smarter, and faster.

But now? The traditional RAG methodology can be retired .

By feeding your data into Google's new model, you can get better results in a simpler way.

If you have documents that need to be analyzed right now, you might as well try it now.

Maybe you will be pleasantly surprised to find that everything becomes so simple.