Projects / Medical Assistant
Generative AI / RAG · Healthcare AI

Medical Assistant

Built a retrieval-augmented assistant that grounds LLM responses in trusted medical reference materials using document processing, embeddings, semantic search, vector storage, and controlled prompting.

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Business problem

Why this project matters

Built a retrieval-augmented assistant that grounds LLM responses in trusted medical reference materials using document processing, embeddings, semantic search, vector storage, and controlled prompting.

Project scope

What the work covered

01Prepare and chunk trusted reference documents
02Generate embeddings and build a searchable vector index
03Retrieve context by semantic similarity
04Construct grounded prompts with source context
05Evaluate response relevance, grounding, and safety
Workflow

From question to evidence

Business problem
→
Data preparation
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EDA / feature work
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Model or analysis
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Evaluation
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Business conclusion
Analysis dashboard

Portfolio evidence areas

These bars summarize the emphasis of the completed work; detailed numeric outputs and full notebook cells remain available in the linked repository and HTML report.

Document prep
72%
Retrieval
86%
Prompt control
81%
Grounding
90%
RAG architecture

Grounded answer-generation workflow

Trusted referencesMedical documents and source material
→
Chunk and embedText preprocessing and vector representations
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Semantic retrievalRelevant context selected for each question
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Controlled promptQuestion combined with retrieved evidence
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Grounded responseAnswer constrained by supplied context
Why no notebook screenshots are shown: the published report does not contain a clean, project-specific visual suitable for portfolio display. The repository remains linked for full implementation details.
Conclusions

What the analysis demonstrated

Retrieval quality determines answer usefulness more than prompt length alone.
Source-grounded prompts reduce unsupported responses and improve traceability.
Evaluation must include both retrieval relevance and generated-answer quality.
GitHub README preview

Repository at a glance

medical-assistant-rag

RAG medical assistant with document processing, vector retrieval, controlled prompting, and grounded response generation.

READMENotebook / reportSource filesProject visuals
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