RAG vs the buzz: How Retrieval-Augmented Generation is quietly disrupting AI

As a Product Manager leading AI innovations at Lokalise, I’ve been closely following the latest AI news and filtering out the noise that inevitably comes with a revolutionary tech boom.

AI has moved incredibly fast since ChatGPT exploded into the mainstream in late 2022, what I like to call ‘the GPT moment’. 

We’ve seen major model releases roughly every few months, from GPT-3.5 through GPT-4, GPT-4o, and most recently GPT-5 with its integrated reasoning capabilities launched in August 2025. Add to that the rapid iterations from Anthropic’s Claude series, Google’s Gemini, and other major players. The pace of change has been relentless.

A few months ago, everyone was talking about fine-tuning, now it’s AI agents, and the conversation keeps shifting to the next big thing. 

Meanwhile, I continue to talk about a framework that’s been around since 2020: Retrieval-Augmented Generation, or RAG. It was widely discussed in AI circles early on but seems to have been overshadowed by newer, flashier developments.

Despite its fleeting moment in the spotlight, RAG continues to deliver tremendous practical impact for businesses looking to make AI actually useful for real-world applications. It bridges the gap between a model that sounds smart and a system that actually knows things.

So, what is RAG? 

Retrieval-Augmented Generation (RAG) is an AI framework for retrieving facts. It was developed by Patrick Lewis and colleagues from the former Facebook AI Research (now Meta AI), University College London and New York University in 2020. 

As the name suggests, RAG has two phases: retrieval and content generation.

How does RAG work?

It’s like giving the AI access to a library. Instead of making AI memorize everything, the model looks up relevant information in real time from a connected knowledge base (e.g., style guide, translation history, glossary), retrieving the right information when needed, and generating a response based on both what it knows and what it just found. 

Steps involved in RAG:

1. Data preparation: Data sources are converted into a format systems can understand
2. Query processing: Query is converted into a format the system can understand
3. Retrieval: The system finds the most relevant documents that match user’s query 
4. Augmentation: The retrieved information is combined with the original query to create an enhanced prompt 
5. Generation: Large language models generate a response based on both its training data and the retrieved information

What’s key is how ‘smart’ the retrieval part is.

RAG in action: AI translation with style consistency

Let’s say we need to translate the English phrase, “Get started with our premium features”, into German for a SaaS company’s onboarding flow.

Without RAG, AI might produce a grammatically correct but generic translation:

“Beginnen Sie mit unseren Premium-Funktionen” 

With RAG, the system retrieves relevant context:

1. Translation Memory: Past translations from the same customer showing they prefer “Loslegen” over “Beginnen” for “Get started” to match their casual brand tone
2. Glossary: Customer guidelines indicating they use “Pro-Features” instead of “Premium-Funktionen” to maintain brand consistency

The RAG-enhanced result: “Loslegen mit unseren Pro-Features”

This translation maintains the customer’s established voice, uses their preferred terminology, and fits the UI context, all because RAG retrieved and applied the right historical examples and guidelines during generation.

The difference between fine-tuning and RAG

You’ve probably heard a lot about fine-tuning, which is similar to RAG in that it also uses data to inform and improve an AI system’s output. However, there are some major differences in how each one works, with RAG outperforming fine-tuning in many instances:

Fine-tuning adjusts a model’s internal knowledge using your specific data, training an LLM on domain-specific data. It’s like teaching someone new skills by having them practice until the knowledge becomes second nature.

RAG keeps the model unchanged but gives it access to external information during the retrieval step, giving the LLM an external data source in real-time.

Here’s a breakdown of how fine-tuning and RAG are different:

Aspect	Fine-tuning	RAG
How it works	Adjusts the model’s internal knowledge	Adds external context via real-time retrieval
Data updates	Requires re-training	Can update knowledge instantly
Model changes e.g. GPT, Claude	Requires re-training	Can be used instantly without any or only minor changes
Flexibility	Limited to training data	Dynamic access to current information
Transparency	Black box approach	Can cite sources and show retrieval

For most enterprise applications, RAG offers better flexibility and maintainability. You can update your knowledge base without retraining models, and you get source attribution for better trust and debugging.

The main benefits and limitations of RAG?

Benefits of RAG

• Real-time accuracy: RAG allows developers to provide the latest research, statistics, or news to the generative models. They can use RAG to connect the LLM directly to live social media feeds, news sites, or other frequently-updated information sources.
• Source attribution: RAG allows the LLM to present accurate information with source attribution. The output can include citations or references to sources. This builds trust and enables fact-checking.
• Reduced hallucinations: By grounding responses in retrieved facts, RAG significantly reduces the model’s tendency to generate plausible-sounding but incorrect information.
• Cost efficiency: RAG extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledge base, all without the need to retrain the model. This saves computational resources and time.
• Domain specialization: Organizations can instantly make their AI systems experts in specific domains by connecting them to relevant knowledge bases, whether that’s medical literature, legal documents, or internal company policies.

Challenges and limitations of RAG

While RAG is powerful, it’s not without challenges:

• Data quality dependency: RAG is only as good as the quality and completeness of the knowledge base. Outdated or incorrect information in the database leads to poor outputs.
• Retrieval quality: RAG depends on the ability to enrich prompts with relevant information, but poor retrieval can lead to irrelevant or insufficient context.
• Latency overhead: Adding a retrieval step can slightly increase response time. The system needs to search through potentially massive databases before generating responses.

Here’s how RAG is delivering impact in different industries

There are many real-world applications of Retrieval Augmented Generation, but here are some of the most common ones:

Customer support

When a customer asks for help, RAG-powered chatbots can retrieve a customer’s history, context about what plan they’re on, what features they’ve used, and connect it with the knowledge base for customer support. With this information provided to the large language model, the answer is much more precise, much more targeted, much more personalized. In many cases, it’s beating human responses.

Translation and localizationIn localization, RAG allows AI systems to retrieve past human-reviewed translations, style guides, glossaries, translation memories, and even descriptions and screenshots. This ensures consistency and strongly improves AI translation quality while dramatically speeding up the translation process.

💡Our RAG solution has proven to improve translation quality comparable to human translators across multiple languages. (90% first-pass acceptance rates, the same as human translations).

Healthcare

RAG systems can access the latest medical research, drug interaction databases, and patient histories to support clinical decision-making while maintaining patient privacy and regulatory compliance.

Finance 

RAG systems can retrieve real-time market information, regulatory filings, and research reports to provide up-to-date financial analysis and recommendations.

Getting started with RAG

For teams looking to implement RAG, here’s how I tested different approaches with customers to find what actually worked:

1. Assessment phase: Identifying pain points and data sources
2. Pilot setup: Starting with high-volume, low-risk content
3. Knowledge base curation: Building and maintaining data sources
4. Evaluation metrics: Measuring success and iterating
5. Scaling strategy: Expanding to more use cases

At Lokalise, we’re building smart AI systems that know how to find and use information when they need it. Tune into our AI series to discover the emerging trends and technologies that will shape the future of global communication.