Definition & Overview
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Author Q&A: Expert Perspectives
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RAG (Retrieval-Augmented Generation)
Key Takeaways
- Prevents AI "hallucinations" by using verified source docs.
- Ensures data privacy by not training public models on your data.
- Allows employees to query handbooks and policies in natural language.
- Updates instantly whenever a policy document is changed.
Why It Matters
RAG turns generic AI into a "Company Expert." It provides employees with instant, accurate answers while keeping sensitive internal data secure.
Interactive Insight
Success Timeline
Retrieve
Find the right policy section.
Augment
Provide context to the AI model.
Generate
Create a human-friendly answer.
Sujith Sasidharan
CTO & Technical Architect
Does RAG share my data with ChatGPT?
No. RAG retrieves your data locally and uses the model only to summarize the answer, never to train public models.
Is it more accurate than regular AI?
Yes, because it is forced to cite its sources from your own company documents.
How fast is it?
Answers are typically generated in under 2 seconds.
Can it handle unstructured data like PDFs?
That is its specialty—turning messy PDFs and emails into searchable wisdom.
What if my policy is outdated?
The AI will only know what is in the document. It is a great tool for identifying where policies need updates.
Does it support multiple languages?
Yes, LLMs can translate the retrieved information into the employee's preferred language.
Can it handle complex HR queries?
It can explain policies, but for personal data like salary, it works with RBAC to ensure security.
Do I need a data scientist to set it up?
Platforms like Kiework handle the RAG engineering for you—you just upload the docs.
What is the 'Augmented' part?
It means the AI is augmented with specific facts before it starts talking.
Why is it the gold standard for HR AI?
Because in HR, 'almost accurate' isn't good enough. You need the exact policy.
Suggested Questions
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