Solving the core challenges of Retrieval-Augmented Generation
Towards Data Science
1 hour ago
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· Pain Point 1: Missing Content
· Pain Point 2: Missed the Top Ranked Documents
· Pain Point 3: Not in Context — Consolidation Strategy Limitations
· Pain Point 4: Not Extracted
· Pain Point 5: Wrong Format
· Pain Point 6: Incorrect Specificity
· Pain Point 7: Incomplete
· Pain Point 8: Data Ingestion Scalability
· Pain Point 9: Structured Data QA
· Pain Point 10: Data Extraction from Complex PDFs
· Pain Point 11: Fallback Model(s)
· Pain Point 12: LLM Security
Inspired by the paper Seven Failure Points When Engineering a Retrieval Augmented Generation System by Barnett et al., let’s explore the seven failure points mentioned in the paper and five additional common pain points in developing an RAG pipeline in this article. More importantly, we will delve into the solutions to those RAG pain points so we can be better equipped to tackle those pain points in our day-to-day RAG development.
I use “pain points” instead of “failure points” mainly because those points all have corresponding proposed solutions. Let’s try to fix them before they become failures in our RAG pipelines.
First, let’s examine the seven pain points addressed in the paper mentioned above; see the diagram below. We will then add the five additional pain points and their proposed solutions.
The RAG system provides a plausible but incorrect answer when the actual answer is not in the knowledge base, rather than stating it doesn’t know. Users receive misleading information, leading to frustration.
We have two proposed solutions: