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
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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.
RAG architecture
Grounded answer-generation workflow
Trusted referencesMedical documents and source material
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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.
Next step
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Use the project inquiry form with Medical Assistant preselected. Include the business objective, available data, timeline, and desired output.