Fraunhofer Society
Research / Public Sector · 2024
Private RAG Pipeline for Research Knowledge
Participated in building a private Retrieval-Augmented Generation pipeline using Fraunhofer's public data to improve AI response accuracy and knowledge accessibility.
Context
Starting point and environment
Research institutions generate vast quantities of publications, technical reports, and project documentation. Making this knowledge searchable, reliable, and accessible is a persistent challenge.
Challenge
Core constraint to solve
Build a RAG system that maintains accuracy and security — resistant to hallucinations and prompt injection — while handling diverse document types and ensuring responses are grounded in verified institutional knowledge.
Solution
Design and implementation approach
Led improvements in data ingestion and cleaning, retrieval processes, and guardrail design. Applied prompt engineering techniques to enforce factual grounding, reduce hallucinations, and strengthen robustness against prompt attacks — ensuring reliable and secure AI-powered knowledge management.
Outcome
Measurable impact and delivery result
Improved response accuracy and reduced hallucination rates. Established security patterns for AI systems handling sensitive research data, including defenses against prompt injection attacks.
Key Takeaway
“RAG quality comes mostly from data prep, not model tweaks. Better ingestion and chunking moved accuracy most, and security guardrails had to be built in from day one.”
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