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:
- Data preparation: Data sources are converted into a format systems can understand
- Query processing: Query is converted into a format the system can understand
- Retrieval: The system finds the most relevant documents that match user’s query
- Augmentation: The retrieved information is combined with the original query to create an enhanced prompt
- 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 a fintech company needs to translate "Verify your identity" into Spanish for their mobile app.
Without RAG, a generic AI might produce: "Verifica tu identidad" (informal)
With RAG, the system retrieves context showing the company uses formal tone:
- Context like, previous translations (from translation memory): "su cuenta" (your account), "su tarjeta" (your card)
- Style guide: Maintain respectful, formal communication
The RAG-enhanced result: "Verifique su identidad"
Now imagine the company rebrands to appeal to younger users and switches to informal communication.
Simply update your translation memory with informal examples ("tu cuenta", "tu tarjeta"), and RAG immediately adapts. The next translation automatically uses "Verifica tu identidad" (informal). No retraining required, just instant adaptation to your evolving brand voice, as RAG retrieves and applies 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 allows you to customize your AI model using 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:
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.
- Solves the cold-start problem: Unlike fine-tuning which requires hundreds of training examples per use case, RAG works immediately even with minimal data. Your first quality document, conversation, or record becomes part of the knowledge base, and the next similar query will retrieve and learn from it instantly.
- Quantifiable quality improvements: With high-quality knowledge bases, RAG can improve output accuracy by 10-20 percentage points. However, quality is directly dependent on your source data. Poor reference materials can slightly deteriorate results, making data curation crucial.
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 localization in 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.
