Overview
A retrieval-augmented assistant that lets product team members ask natural-language questions about US regulations and safety laws and get grounded answers, mapped to product-label requirements.
The problem
Product teams had to manually read and cross-reference lengthy US regulatory documents and safety standards whenever a new rule was introduced. Finding the clauses relevant to a specific product label was slow, repetitive and error-prone.
Context
Built inside an enterprise compliance platform used by product teams. The knowledge base must stay current as new standards are published, and answers must be grounded in the source clauses rather than generic model knowledge.
Constraints & challenges
- 01Regulatory documents are long, hierarchical and dense — naive chunking loses clause context.
- 02Retrieval must surface the exact clause, not a paraphrase, so answers can be verified against the source.
- 03Product-label requirements need to be mapped back to specific regulatory sections.
- 04The assistant has to integrate with existing legacy systems and workflows.
- 05The knowledge base must be refreshed safely as new standards arrive.
My role
AI/ML engineer owning the RAG architecture end-to-end: ingestion and chunking strategy, embedding and vector-search design, retrieval and prompt pipeline, and the LLM chat interface for product teams.
The solution
Designed a RAG pipeline over US regulatory documents. Documents are ingested, structurally chunked and embedded into a vector store; semantic vector search retrieves relevant regulatory clauses at query time and an LLM composes grounded answers and compliance summaries with citations to the source sections. A dedicated ingestion path keeps the knowledge base current as standards change.
- Natural-language querying of US regulations and safety standards
- Semantic retrieval of relevant regulatory clauses
- Mapping of clauses to product-label requirements
- Compliance summary generation
- Incremental knowledge-base updates as standards change
System architecture
System architecture
- Source
- Processing
- Model / LLM
- Storage
- Interface
- API / Service
- Output
Ingestion runs as a separate path from query-time retrieval so new standards can be added without redeploying the chat service. Retrieved clauses are passed to the LLM with section metadata so every answer can cite its source.
Key engineering decisions
- 01
Structure-aware chunking over fixed windows
Regulations are hierarchical (parts, sections, clauses). Chunking along that structure keeps each retrieved passage self-contained and citable.
- 02
Metadata-rich vector index
Storing section identifiers and document versions alongside embeddings lets retrieval filter by standard and lets answers point back to the exact clause.
- 03
Grounded generation with citations
The LLM only answers from retrieved clauses and returns references, so product teams can verify every claim against the source text.
- 04
Decoupled ingestion pipeline
Keeping ingestion separate from serving means the knowledge base can be refreshed as new standards are introduced with no downtime.
Technology
- Python
- LangChain
- RAG
- Vector Database
- Embedding Models
- LLM APIs
- FastAPI
- Docker
- Legacy System Integration
Results
Turns hours of manual regulatory research into grounded conversational answers, reducing research effort for product teams while keeping every answer traceable to the source standard. The system is actively in development.
Lessons learned
- Retrieval quality is decided at ingestion time — chunking and metadata matter more than the choice of LLM.
- Citations are a product requirement in compliance, not a nice-to-have.
- Plan the refresh path for the knowledge base before the first document is indexed.
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