The Challenge
Biotech regulatory documents are dense, interconnected, and constantly evolving. Compliance teams spend hours searching through thousands of pages to answer specific questions. A single missed clause can mean failed audits or delayed product launches.
I built a RAG (Retrieval-Augmented Generation) system that lets professionals ask natural language questions and get accurate, source-cited answers in seconds.
What is RAG?
RAG combines two powerful ideas:
- Retrieval — Find the most relevant document chunks using semantic search
- Generation — Use an LLM to synthesize a coherent answer from those chunks
This approach solves the LLM hallucination problem by grounding responses in actual source documents.
Architecture
The system has three main components:
1. Document Ingestion Pipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", ". ", " "],
)
# Split documents into overlapping chunks
chunks = splitter.split_documents(regulatory_docs)
# Generate embeddings and store in vector database
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Pinecone.from_documents(chunks, embeddings, index_name="regulations")2. Retrieval with Semantic Search
Instead of keyword matching, the system uses vector embeddings to find semantically similar content. A query like "What are the labeling requirements for Class II devices?" retrieves relevant chunks even if they don't contain those exact words.
3. Answer Generation with Source Citations
The LLM receives the retrieved chunks as context and generates an answer with specific references to source documents, sections, and page numbers.
Key Design Decisions
- Chunk size of 1000 tokens — Large enough for context, small enough for precision
- 200-token overlap — Prevents losing information at chunk boundaries
- Hybrid search — Combined dense (embedding) and sparse (BM25) retrieval for better recall
- Source tracking — Every chunk carries metadata (document name, section, page) for citations
Results
The system handles questions across multiple regulatory frameworks with high accuracy. Compliance teams reported:
- 80% faster document research compared to manual search
- Source citations in every answer build trust and enable verification
- Consistent answers — no more conflicting interpretations from different team members
Lessons Learned
- Chunk quality matters more than model quality — Bad chunking leads to bad retrieval, which no amount of LLM power can fix.
- Evaluation is essential — Build a test set of question-answer pairs early. Without it, you're optimizing blindly.
- Users need transparency — Showing source documents alongside answers was the feature that drove adoption.
RAG systems are not just a trend — they're a practical solution for knowledge-intensive domains where accuracy and traceability are non-negotiable.