Exploring 12 RAG Pain Points and Their Solutions

In a recent article by Wenqi Glantz on Towards Data Science, the challenges and solutions in developing Retrieval-Augmented Generation (RAG) systems are thoroughly examined. Inspired by the paper “Seven Failure Points When Engineering a Retrieval Augmented Generation System” by Barnett et al., Glantz not only delves into these seven core challenges but also identifies five additional common pain points in RAG development.

  1. Missing Content: The RAG system may give plausible but incorrect answers if the real answer isn’t in the knowledge base. Solutions include data cleaning and better prompting to handle lack of information.
  2. Missed the Top Ranked Documents: Essential documents might not appear in the top results, leading to inaccurate responses. Solutions involve hyperparameter tuning and reranking retrieval results for improved performance.
  3. Not in Context – Consolidation Strategy Limitations: When documents with the answer don’t make it into the context for generating an answer, tweaking retrieval strategies and finetuning embeddings can help.
  4. Not Extracted: Difficulty in extracting correct answers due to information overload. Solutions include data cleaning, prompt compression, and LongContextReorder for better organization of information.
  5. Wrong Format: Failing to extract information in a specific format. This can be rectified through better prompting, output parsing, using Pydantic programs, and OpenAI JSON mode.
  6. Incorrect Specificity: Responses lacking detail or specificity. Advanced retrieval strategies like small-to-big retrieval and recursive retrieval can be effective here.
  7. Incomplete: Partial responses that don’t provide all details. Query transformations such as routing, query-rewriting, and sub-questions can provide more comprehensive answers.

In addition to these seven points from the paper, Glantz adds five more:

  1. Data Ingestion Scalability: Handling large volumes of data efficiently. Parallelizing the ingestion pipeline is a proposed solution.
  2. Structured Data QA: Challenges in retrieving relevant structured data. Solutions include Chain-of-table Pack and Mix-Self-Consistency Pack.
  3. Data Extraction from Complex PDFs: Difficulty in extracting data from embedded tables in PDFs. Embedded table retrieval using specialized tools can help.
  4. Fallback Model(s): Need for backup models in case of primary model malfunctions. Solutions include using Neutrino router and OpenRouter.
  5. LLM Security: Addressing prompt injection and insecure outputs. Llama Guard, a tool for classifying content, is suggested.

Glantz’s article is an extensive guide for those involved in RAG development, offering practical solutions to enhance the effectiveness and efficiency of these systems. This comprehensive overview is valuable for both novices and experts in the field.

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