How to Improve RAG Model Performance with Synthetic Data

Effective strategies for leveraging high-quality synthetic data to improve RAG model performance.

Living in today's AI-driven world, you can't escape the buzz surrounding one of the most efficient methods to tweak language models and introduce new knowledge sources: Retrieval Augmented Generation (RAG). Optimizing these models with trusted data is key to their successful deployment and scalability in production environments. In this blog, we highlight effective strategies for leveraging high-quality synthetic data to improve RAG model performance, resulting in potential cost, resource, and time savings over traditional data acquisition methods. 

Ready to join 125k+ developers in the synthetic data revolution? Get started for free using Gretel's synthetic data platform today. 

What is Retrieval-Augmented Generation?

Retrieval Augmented Generation (RAG) combines the capabilities of LLMs with contextual information retrieval from external data sources to generate more accurate and enriched responses. It provides a cost-efficient and flexible way to introduce new knowledge sources to LLMs and improve domain-specific knowledge, generating more trustable and reliable responses.

Figure 1. The above image shows an end-to-end RAG model pipeline, including the underlying logic from user prompt to response. 

What advantages do RAG models offer over traditional LLMs?

LLMs are a wildly popular method of using generative AI to produce novel text. They have been applied across industries to a variety of use cases, including semantically relevant information retrieval, code generation, sentiment analysis, text summarization, and content classification. Despite these benefits, organizations face a few common challenges when using LLMs, including:

  • The risk of hallucinations or presenting inaccurate or false information.
  • Generating outdated information that only reflects insights from stale training data.
  • Relying on non-trusted resources to produce seemingly credible answers.
  • Expensive infrastructure and engineering costs associated with LLM fine-tuning

RAG models have proven to be more scalable and cost-efficient than traditional LLM fine-tuning methods for a domain-specific task. Because RAG models are sourcing contextually relevant answers from a trusted knowledge source rather than the entire web, they tend to increase trustworthiness and reduce errors in LLMs. The benefits of RAG models include:

  • Contextual and Domain Relevance: Integrating external data, improves the contextual relevance of responses. RAG models excel in specialized queries by accessing relevant external knowledge, surpassing LLMs' training limitations.
  • Reduced Hallucinations: Dynamic information updates in RAG models, mitigates the risk of generating incorrect content.
  • Cost-efficient Scalability and Customization: RAG models offer scalable knowledge integration and customizable response generation without the need to retrain the core LLM.
  • Versatile and Flexible Q&A Approach: RAG models offer a modular generation of Q&A pairs using different, specialized models, enhancing accuracy and effectiveness of responses.

How can synthetic data augment RAG model performance?

Building RAG models involves steps common to other LLMOps pipelines, with a few added steps for ensuring quality responses from newly introduced knowledge sources. Typical workflows start with data collection and indexing, followed by fine-tuning and testing a model before putting it into production. In addition there is also a level of retrieval optimization and response testing to help ensure your RAG model is reliable across a wide variety of applications.

Figure 2. Similar to the MLOps lifecycle, enhancing a RAG model with synthetic data results in continuous improvements across the different stages of data collection, fine tuning, evaluation, and testing.

Data collection

Often the most challenging part about training a model is not just collecting a sufficient quantity of data, but also ensuring the right type and variety for effective model training. This is especially true for RAG models, since the collected data will serve as a trusted resource from which the model will retrieve responses to novel prompts and questions. 

  • RAG Model Bootstrapping: Use synthetic datasets for initial RAG model development when real data is scarce or sensitive.
  • Domain Enhancements: Enhance RAG models with domain-specific topics and styles for better real-world query performance.
  • Diversity and Ethics: Foster cultural, ethical, and linguistic diversity in RAG models for equitable and context-aware responses.

Expanding Knowledge Sources and Indexing

After initial knowledge collection, expanding the knowledge base and implementing dynamic indexing are key pre-processing steps to your RAG training data strategy. 

  • Knowledge Base Expansion: Use synthetic data to broaden knowledge bases, bridging informational gaps and enhancing model references.
  • Dynamic Indexing: Leverage synthetic data to craft and refine flexible indexing strategies for improved retrieval accuracy.


Retrieval is the most crucial part of RAG systems. It allows these models to seamlessly integrate contextual information, enhancing the generation of responses by pulling in relevant data. 

  • Semantic Search Enhancement: Utilize synthetic queries to test and enhance semantic search algorithms for varied intents and complexities.

Large Language Model Fine-tuning

Fine-tuning involves adjusting model parameters based on specific tasks or domains AND optimizing performance for targeted applications. This iterative process refines the RAG model's capabilities, tailoring it to excel in specialized operational settings.

  • Privacy-conscious Fine-tuning: Employ differential privacy in LLM fine-tuning for secure adaptation to real-world data.
  • Prompt Augmentation: Enhance understanding and response generation with synthetic data.

RAG Responses

Enhancing the quality of RAG responses is key to scaling solutions effectively. This ensures the ability to deliver precise and relevant responses, catering to the distinct requirements of various enterprises.

  • Response Enrichment: Utilize synthetic data for adding context and realism to responses.
  • Tabular Augmentation: Enhance responses with synthetic tabular data for better visuals and analytics.

Evaluation and Testing

Generalizability is a fundamental aspect of RAG models to ensure they consistently deliver accurate and useful responses. It is essential to test and evaluate a number of possible prompt styles and responses to ensure the model satisfies diverse input prompts while maintaining consistency. 

  • Edge Case Evaluation: Use synthetic scenarios to assess model performance on rare events and anomalies.
  • Hallucination Detection: Utilize synthetic data to evaluate the model's ability to identify and mitigate hallucinated content.
  • Adversarial Challenges: Test model resilience with synthetic adversarial inputs for robust reasoning and application.

Ready to scale your RAG model with synthetic data?

As demonstrated above, synthetic data provides significant value to developers building RAG systems across the entire LLMOps lifecycle, resulting in significant savings over traditional data acquisition methods while providing realizable gains in model performance. 

We invite you to join over 125k developers generating synthetic data with Gretel today. Remember, it’s free to sign up for a Gretel account. Don’t forget to share your learnings with the rest of the AI community or reach out to the Gretel team for support in our community Discord. Happy synthesizing!