Introduction
Retrieval-augmented generation (RAG) is a powerful approach that enhances the capabilities of large language models by integrating real-time information retrieval. This integration helps overcome language models' limitations in accessing up-to-date or domain-specific data, making RAG a cornerstone of AI-driven solutions today. From ambitious scale-ups to global enterprises, RAG has become the number one method for companies to provide their internal data to AI systems, ensuring accurate, context-aware, and grounded responses. By effectively utilizing retrieval mechanisms, RAG systems can unlock new levels of relevance and precision in AI applications.
However, building a robust RAG system requires careful design and optimization of various components. Below are ten battle-tested tips to help you improve the quality and reliability of your RAG system.
1. Improve your Chunking
Chunking is essential for processing large documents that exceed the input size limits of language models. It involves breaking the text into smaller, manageable pieces, but doing so effectively is challenging. Poorly chosen chunk sizes can lead to loss of critical context or fragmented information, reducing retrieval accuracy. To address this, determine the chunk size based on the nature of your data. Larger or overlapping chunks help preserve context, particularly important for sequential information like narratives or legal texts. Experiment with chunk sizes and overlap percentages to find the optimal balance for your corpus, ensuring better retrieval and continuity.
Resources:
https://adasci.org/chunking-strategies-for-rag-in-generative-ai/
2. Experiment with Different Embedding Models
Try different embedding models—such as OpenAI or Cohere embeddings, open-source, or domain-specific embeddings—to identify which performs best for your use case. These models vary in their ability to capture semantic nuances, computational efficiency, and adaptability to your dataset. Experimenting with various embeddings will help you find the most suitable one to improve retrieval quality.
Resources:
https://huggingface.co/spaces/mteb/leaderboard
https://platform.openai.com/docs/guides/embeddings
https://cohere.com/embeddings
3. Combine Semantic Embeddings with Exact Matching
Use both BM25 and semantic embeddings for retrieval. BM25, short for Best Matching 25, considers the frequency of keywords (terms) in a document and adjusts for the length of the document to avoid favouring longer texts. BM25 works well to match specific phrases like error codes or technical terms, while embeddings help retrieve semantically related content. Combining them—either by merging their results or using a hybrid ranking approach—allows for more comprehensive retrieval, providing balanced coverage of both specific and broad matches.
4. Introduce Contextual Embeddings
Enhance retrieval accuracy by adding contextual information to each chunk before embedding it. This involves prepending a brief, context-specific explanation or metadata—such as document titles, headings, or summary sentences—to each chunk. This strategy helps retain critical details that might be lost during chunking, improving chunk-level retrieval, especially when the key context is fragmented.
Resources:
https://www.anthropic.com/news/contextual-retrieval
5. Apply Reranking Models for Improved Context Use
After retrieving the top results, use reranking models—such as those based on BERT or other transformer architectures—to refine which chunks are passed to the model for response generation. Reranking focuses on the most relevant information by reassessing the initial retrievals with deeper semantic understanding, reducing latency and improving overall response accuracy. This is particularly important for large knowledge bases where initial retrieval may include many chunks.
Resources:
https://cohere.com/rerank
6. Fine-tune your Embedding Model and Reranker
To maximize retrieval quality, consider fine-tuning your embedding model, reranker, or both on domain-specific data. Tailoring the retriever to your unique data helps it understand content and language patterns specific to your corpus, thereby improving retrieval precision. A fine-tuned reranker ensures that the most contextually relevant information rises to the top, refining the selection of chunks before they are passed to the generation stage. Adjusting either component—or both—to align with your use case can significantly enhance retrieval accuracy.
7. Introduce Few-Shot Examples for Better QA Performance
Including few-shot examples in prompts can help the model generate higher-quality responses, especially in Q&A scenarios. Select relevant examples that demonstrate the desired response format. Prompts with these carefully chosen examples and explicit instructions significantly improve recall rates and overall performance by guiding the model toward more accurate and contextually appropriate answers.
Resources:
https://www.promptingguide.ai/techniques/fewshot
8. Self-Reflective RAG for Enhanced Adaptivity
Self-reflective RAG offers an adaptive retrieval mechanism that dynamically enhances response accuracy by evaluating and self-critiquing retrieved content. Unlike traditional RAG systems, which retrieve a fixed set of passages regardless of relevance, SELF-RAG uses reflection tokens to signal when retrieval is necessary and to critique the quality of both retrieved passages and generated text. This enables the system to select only pertinent information, improving factuality without sacrificing flexibility. By generating critique tokens, SELF-RAG can iteratively refine responses, balancing retrieval needs with content accuracy, making it especially effective in open-domain QA, reasoning, and fact verification.
Resources:
https://arxiv.org/abs/2310.11511
9. Corrective RAG (CRAG) with Retrieval Evaluation
Corrective RAG (CRAG) enhances the robustness of retrieval-augmented generation systems by implementing a retrieval evaluator to assess the quality of retrieved documents. The evaluator assigns confidence levels to indicate whether documents are accurate, incorrect, or ambiguous. For incorrect retrievals, CRAG expands the search with large-scale web sources to retrieve more relevant information, thereby preventing reliance on suboptimal data. Additionally, CRAG refines document content through a decompose-then-recompose algorithm, filtering out irrelevant information and retaining only essential insights.
Resources:
https://arxiv.org/abs/2401.15884
10. Integrate External Tools for Complex Computations
Integrating external tools (such as calculators) for handling complex computations or domain-specific tasks allows the system to effectively manage calculations beyond its built-in capabilities, improving accuracy and expanding the scope of questions the RAG system can handle.
Resources:
https://platform.openai.com/docs/guides/function-calling
Conclusion
Building a robust RAG system involves carefully tuning each component—from retrieval to generation—while ensuring the two work together seamlessly. By focusing on contextual relevance, adaptability, speed, diversity, and continuous improvement, your RAG system can deliver highly grounded and reliable outputs, mitigating risks like hallucinations and enhancing user trust.
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