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

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.

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

Lessons from implementation: 

The key differentiator between successful and unsuccessful RAG implementation is data quality. 

In our testing across various use cases, organizations with well-maintained knowledge bases saw 10-20% improvements in output quality immediately. Those with inconsistent, outdated, or low-quality reference data saw minimal gains or slight quality decreases. 

Start by auditing your knowledge assets. Even simple filtering criteria (e.g., "only retrieve from documents updated in the last year" or "exclude content flagged for revision") can dramatically improve RAG performance. 

Prioritize curated, verified content over raw data dumps. Tag high-quality sources, use reviewed materials over automatically generated content, and identify specific datasets or document repositories known for accuracy and relevance. 

Remember: RAG doesn't make bad data good. It amplifies whatever quality exists in your knowledge base.

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.