Quality assurance · Production

AI4DQ Unstructured: Solving data quality for gen AI applications

The problem

Organizations building gen AI applications face significant data quality issues in unstructured document corpora — including diverse formats that are hard to parse, lack of metadata, siloed storage, conflicting document versions, irrelevant or duplicate content, multiple languages, and unfiltered sensitive information — which cause downstream hallucination, information loss, wasted compute, and compliance risk.

Workflow diagram · grounded in source
1
Corpus scan and flagging
trigger
“AI4DQ Unstructured scans through the input corpus across the three dimensions and flags the documents that need attention”
2
Document clustering and labeling
ai_action
“AI4DQ Unstructured combines NLP techniques with the power of gen AI to: Train custom embeddings on top of the existing corpus. Cluster the documents using the embeddings to classify them based on semantic meaning. Label each document clu…”
3
Duplicate document detection
ai_action
“AI4DQ Unstructured automatically identifies and presents duplicated/versioned document for human review by: Creating and extracting metadata to describe each document. Comparing documents against to establish pair-wise duplicates. Resolv…”
4
Human review of flagged documents
human_review
“Offering the human in-the-loop a view of potential duplicated/versioned documents and recommending appropriate correction strategies”
5
Data quality scoring output
output
“A scoring mechanism presents individual data quality scores to give a view of the overall unstructured data quality of the corpus”
Reported outcome

In a public health deployment, AI4DQ processed 2.5 GB of data across 1,500+ files, identified more than ten high-priority data quality issues, removed 100+ irrelevant or duplicated documents saving 10–15 percent in data storage cost, and preserved information for 5 percent of critical policy documents.
Separately, one project saw a 20 percent increase in RAG pipeline accuracy from the addition of metadata tags.

Reported metrics
RAG pipeline accuracy improvement20 percent
Data storage cost savings10–15 percent
Critical policy documents with preserved information5 percent
Irrelevant/duplicated documents removed100+
Show all 7 reported metrics
RAG pipeline accuracy improvement20 percent
data storage cost savings10–15 percent
critical policy documents with preserved information5 percent
irrelevant/duplicated documents removed100+
data ingested and processed2.5 GB
files analyzed1,500+
high-priority data quality issues identifiedmore than ten
Reported stack
AI4DQNLPLLMRAG
Source
https://medium.com/quantumblack/solving-data-quality-for-gen-ai-applications-11cbec4cbe72
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

In a public health deployment, AI4DQ processed 2.5 GB of data across 1,500+ files, identified more than ten high-priority data quality issues, removed 100+ irrelevant or duplicated documents saving 10–15 percent in da…

What tools did this team use?

AI4DQ, NLP, LLM, RAG.

What results were reported?

RAG pipeline accuracy improvement: 20 percent; Data storage cost savings: 10–15 percent; Critical policy documents with preserved information: 5 percent; Irrelevant/duplicated documents removed: 100+ (source-reported, not independently verified).

How is this quality assurance AI workflow structured?

Corpus scan and flagging → Document clustering and labeling → Duplicate document detection → Human review of flagged documents → Data quality scoring output.

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