Building Cheaper and More Effective RAG with Cleanlab

Building Cheaper and More Effective RAG with Cleanlab

Retrieval-augmented generation (RAG) has emerged as a powerful technique in natural language processing by combining the strengths of information retrieval and text generation to provide more accurate and contextually relevant outputs. However, RAG systems often face challenges such as retrieving irrelevant or low-quality data, managing the efficiency of the retrieval process, and ensuring the reliability of generated responses. These common issues can hinder the overall effectiveness and trustworthiness of RAG pipelines, necessitating solutions that address these critical stages.

Cleanlab significantly enhances the RAG pipeline at every stage—before, during, and after retrieval. In the initial creation phase, Cleanlab ensures the quality of the data by identifying and correcting text data and label errors, which leads to more accurate and reliable documents. During the retrieval stage, Cleanlab increases efficiency by filtering out irrelevant documents, thus speeding up the process and improving the accuracy of the retrieved information. Finally, after retrieval, Cleanlab's trustworthy language model (TLM) assigns a trustworthiness score to each response. This helps eliminate hallucinations and nonsensical answers, ultimately improving the accuracy of the output from any large language model (LLM).

To demonstrate how Cleanlab can be used to enhance a RAG pipeline, let's follow through a practical example:

Processing and Categorizing Documents with Cleanlab Studio

In our example, we have an unorganized collection of documents without any associated metadata tags. Cleanlab Studio extracts the text from each document during ingestion and represents each one as a text example.

1. Initial Setup

1. Load the documents.
2. Create a project to kick off Cleanlab's automated AI model training.
3. Start the data curation process by selecting "Ready for Review".

2. Automated Labeling Workflow

Cleanlab Studio uses an AI plus human-in-the-loop process for labeling:

1. Autolabel documents with the highest confidence scores.
2. Manually label additional documents with lower confidence scores.
3. Re-run the project to kickstart another round of AI model training for better-label suggestions.

This process involves repeatedly improving results by retraining the model with newly labeled data until all documents are categorized accurately.

3. Data Refinement

Cleanlab's unique data-centric AI also identifies issues such as duplicates, outliers, and mislabeled data:

1. Review and resolve mislabeled data.
2. Identify and exclude outliers and duplicates.
3. Continue the process until the data is clean and correctly classified.

Improving RAG Pipeline Efficiency

With the categorized documents, the RAG pipeline can now:

1. Compute embeddings for all documents using a pre-trained sentence transformer.
2. Include topic tags to speed up retrieval times by reducing the number of documents searched.

In a test, retrieving documents including the topic tag ran 40% faster. For larger collections, this speedup can translate into significant time savings.

Enhancing Response Reliability with Trustworthiness Scores

To generate more reliable responses and reduce hallucinations:

1. Use Cleanlab's TLM to produce responses and assign trustworthiness scores.
2. Verify high-trustworthiness scores with actual document content.

If using your own infrastructure, you can still utilize TLM by providing the prompt-response pairs to the get_trustworthiness_score method. This helps benchmark and optimize components of your RAG pipeline.

Conclusion

Cleanlab and its TLM enhance the efficiency and reliability of RAG pipelines by ensuring data quality, speeding up retrieval processes, and providing trustworthy response scores. This holistic approach saves time, reduces errors, and builds more effective RAG systems.

Keywords

• Retrieval-Augmented Generation (RAG)
• Cleanlab
• Trustworthy Language Model (TLM)
• Data Curation
• Natural Language Processing (NLP)
• Document Categorization
• Automated Labeling
• Data Quality

FAQ

1. What is Retrieval-Augmented Generation (RAG)? RAG is a technique in natural language processing that combines information retrieval and text generation to provide accurate and contextually relevant outputs.

2. What challenges do RAG systems face? RAG systems often face challenges like retrieving irrelevant or low-quality data, managing the efficiency of the retrieval process, and ensuring the reliability of generated responses.

3. How does Cleanlab improve the RAG pipeline? Cleanlab improves the RAG pipeline by ensuring data quality, filtering out irrelevant documents, and assigning trustworthiness scores to generated responses to reduce hallucinations and errors.

4. What are the steps involved in using Cleanlab Studio for document categorization? The steps include loading documents, creating a project, starting the automated labeling workflow, and iteratively improving label accuracy through AI-assisted and manual labeling until all documents are correctly categorized.

5. How does Cleanlab enhance retrieval efficiency? By categorizing documents with topic tags, Cleanlab reduces the number of documents searched during retrieval, leading to faster query processing times.

6. What role does Cleanlab's Trustworthy Language Model (TLM) play post-retrieval? TLM assigns trustworthiness scores to generated responses to help identify and reduce hallucinations and nonsensical answers, thereby improving response accuracy.

7. Can I use Cleanlab’s TLM with my existing RAG infrastructure? Yes, you can use TLM to assign trustworthiness scores to responses generated by your existing RAG or LLM infrastructure by providing the prompt-response pairs to the get_trustworthiness_score method.

8. How does Cleanlab handle data issues beyond mislabeling? Cleanlab can automatically detect issues like duplicates, outliers, and inappropriate content such as PII or toxic language, allowing for comprehensive data curation.

By following these guidelines and using Cleanlab’s tools, you can construct more efficient, reliable, and faster RAG pipelines for varied applications.