# Challenges We Faced Building RAG for Genetic Report Interpretation

Genetic reports contain a huge amount of valuable information, but they are often difficult for patients to understand. We provide **over 2000 pages** to customer as their genetic test result!

They have complex medical terminology, long explanations, and inconsistent formatting.

For that reason, we started exploring how Retrieval Augmented Generation(RAG) could help users better understand their reports while still keeping the system reliable and clinically cautious.

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![](https://cdn.hashnode.com/uploads/covers/69fb6b6950ecad45334c325c/714f5cff-a666-4a0c-9a70-351921d199d6.png align="center")

### Why Simple AI Summarization Was Not Enough

At first, using a large language model to summarize reports seemed straightforward. But in practice, we quickly discovered several problems.

*   Miss important medical context
    
*   Long PDF reports were difficult to process consistently
    

Medical AI systems cannot behave like generic chat-bots. Small mistakes in interpretation can create confusion for users.

With this context, we chose to use a more structured RAG-based approach.'

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### Only Early RAG Pipeline

Our current workflow focuses on:

1.  Extracting report content
    
2.  Splitting documents into searchable chunks
    
3.  Retrieving relevant medical context
    
4.  Generating assistive explanations using AI models
    

Instead of replacing medical professionals, the goal is to help users navigate complicated information more clearly.

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![](https://cdn.hashnode.com/uploads/covers/69fb6b6950ecad45334c325c/90deb5d1-9914-483a-91dc-09b6796338b4.jpg align="center")

### Technical Challenges We Encountered

Some of the biggest engineering challenges included

*   **Inconsistent Document Structures**
    

Different clinics and labs format reports differently, making reliable parsing difficult.

*   **Retrieval Noise**
    

Even small retrieval mistakes could lead to irrelevant or incomplete explanations.

*   **Medical Terminology**
    

Genetic terminology is highly specialized.

*   **Privacy and Compliance** ⭐
    

Because health data is sensitive, infrastructure decisions also needed to consider compliance requirements as HIPPA, PIPEDA, and Quebec Law 25.

One of the biggest lessons so far is that healthcare AI systems require much more than simply connecting a language model to a database.

> Reliability, retrieval quality, explain-ability, and careful wording matter just as much as model performance.

We are continuing to improve :

*   retrieval accuracy
    
*   structured medical context handling
    
*   multilingual support
    
*   FHIR-based interoperability
    
*   overvaluation and monitoring workflows
    

This is only the beginning of our journey, and we plan to share more lessons as Ebovir continues building reliable AI systems for healthcare.