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Fixing the core challenges of Retrieval-Augmented Technology
· Ache Level 1: Lacking Content material
· Ache Level 2: Missed the Prime Ranked Paperwork
· Ache Level 3: Not in Context — Consolidation Technique Limitations
· Ache Level 4: Not Extracted
· Ache Level 5: Unsuitable Format
· Ache Level 6: Incorrect Specificity
· Ache Level 7: Incomplete
· Ache Level 8: Information Ingestion Scalability
· Ache Level 9: Structured Information QA
· Ache Level 10: Information Extraction from Advanced PDFs
· Ache Level 11: Fallback Mannequin(s)
· Ache Level 12: LLM Safety
Impressed by the paper Seven Failure Factors When Engineering a Retrieval Augmented Technology System by Barnett et al., let’s discover the seven failure factors talked about within the paper and 5 extra frequent ache factors in growing an RAG pipeline on this article. Extra importantly, we’ll delve into the options to these RAG ache factors so we will be higher geared up to deal with these ache factors in our day-to-day RAG growth.
I exploit “ache factors” as an alternative of “failure factors” primarily as a result of these factors all have corresponding proposed options. Let’s attempt to repair them earlier than they turn into failures in our RAG pipelines.
First, let’s look at the seven ache factors addressed within the paper talked about above; see the diagram under. We are going to then add the 5 extra ache factors and their proposed options.
The RAG system offers a believable however incorrect reply when the precise reply will not be within the information base, slightly than stating it doesn’t know. Customers obtain deceptive data, resulting in frustration.
We now have two proposed options:
Clear your information
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