How To Improve RAG in 6-Steps

Is your RAG system underperforming despite using a top-tier LLM? Many teams focus on model upgrades or prompt tweaks, but often, the most impactful improvements lie elsewhere.

At AZTELA we've identified seven practical strategies—often overlooked—that can significantly enhance your RAG application's performance. These insights are inspired by industry expert Jason Liu's comprehensive guide on optimizing RAG systems .

1. Generate Synthetic Data for Baseline Metrics

Before diving into complex optimizations, establish a performance baseline using synthetic data. Create synthetic questions from your existing text chunks and verify if your system retrieves the correct sources. This approach helps in:

  • Measuring system precision and recall

  • Identifying areas needing improvement

  • Facilitating repeatable testing and evaluation

Implementing this requires minimal effort—just a simple prompt to generate questions and a loop to test retrieval accuracy.

2. Incorporate Date Filters for Timely Results

Users often seek the most recent information. Without date filters, your system might return outdated content. By adding date filters, you can:

  • Enhance the relevance and freshness of search results

  • Improve efficiency in narrowing down results

  • Enable trend analysis and historical context

While this may add a slight processing overhead (approximately 500-700 milliseconds), the improvement in user satisfaction is substantial.

3. Optimize Feedback Mechanisms

Generic feedback prompts like "Did you like our response?" often yield ambiguous insights. Instead, use specific questions such as:

  • "Did we answer your question?"

  • "Was this information helpful?"

This targeted approach provides clearer, actionable feedback, enabling more precise system improvements .

4. Monitor Cosine Distance and Reranking Scores

Tracking metrics like average cosine distance and reranking scores (e.g., Cohere's) can highlight areas where your system struggles. By analyzing these metrics, you can:

  • Identify challenging queries

  • Prioritize improvements

  • Make data-driven decisions for resource allocation

Implementing this involves logging query IDs alongside their respective scores for analysis .

5. Combine Semantic and Full-Text Search

Relying solely on semantic search might miss exact keyword matches. Integrating full-text search (like BM25) alongside semantic search can:

  • Improve retrieval accuracy

  • Enhance the overall effectiveness of the search system

  • Reduce latency compared to generating hypothetical document embeddings

This hybrid approach ensures a more comprehensive search capability .

6. Enrich Chunks with Metadata

Including metadata such as file paths, document titles, authors, creation dates, and tags in your text chunks can:

  • Improve search relevance by leveraging additional context

  • Enable filtering and narrowing down search results based on metadata fields

  • Enhance understanding of document structure and hierarchy

This requires modifying your chunking process to append relevant metadata, slightly increasing storage requirements but significantly boosting search quality .

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