Curing LLM Hallucinations: Building a Production-Grade Medical RAG with PubMed and Hybrid Search

A developer built a production-grade Medical Retrieval-Augmented Generation (RAG) system that cures LLM hallucinations by grounding clinical AI responses in peer-reviewed evidence from the PubMed API. The system implements Hybrid Search, combining BM25 keyword precision with vector search semantic depth using LlamaIndex, Pinecone, and Elasticsearch, and enforces strict citation requirements through a prompt template. By fusing results from both retrieval methods via Reciprocal Rank Fusion and a cross-encoder re-ranker, the system delivers evidence-based medical answers with source citations.

Ever asked an AI for a medical dosage recommendation only to get a confident-sounding but dangerously incorrect answer? In the world of healthcare, LLM hallucinations aren't just "bugs"—they are critical risks. To bridge the gap between static training data and the rapidly evolving world of clinical research, we need a robust Medical RAG Retrieval-Augmented Generation system. By implementing Hybrid Search combining the keyword precision of BM25 with the semantic depth of Vector Search , we can ground our models in peer-reviewed evidence from the PubMed API . In this guide, we will leverage LlamaIndex , Pinecone , and Elasticsearch to build a Clinical Decision Support system that prioritizes accuracy and real-time knowledge retrieval. 🚀 Standard RAG pipelines often rely solely on cosine similarity in a vector space. However, medical queries are unique: Here is how our system handles a medical query, ensuring we get the best of both worlds: keyword matching and semantic context. php graph TD User User Query -- Router{LlamaIndex Router} subgraph Retrieval Layer Hybrid Search Layer Router -- |Keyword Search| ES Elasticsearch - BM25 Router -- |Semantic Search| PC Pinecone - Vector DB end ES -- |Top K Results| Reranker Cross-Encoder Re-ranker PC -- |Top K Results| Reranker subgraph Knowledge Source Data Ingestion PM PubMed API -- Clean Data Cleaning Clean -- ES Clean -- PC end Reranker -- |Contextual Chunks| LLM GPT-4o / Clinical LLM LLM -- |Evidence-Based Response| Output Final Answer + Citations To follow this tutorial, you'll need: We use the PubMed API to fetch the latest research papers. Using Biopython or direct REST calls, we extract the title and abstract. python from llama index.core import Document from Bio import Entrez def fetch pubmed abstracts query, max results=10 : Entrez.email = "your@email.com" handle = Entrez.esearch db="pubmed", term=query, retmax=max results record = Entrez.read handle ids = record "IdList" documents = handle = Entrez.efetch db="pubmed", id=",".join ids , rettype="abstract", retmode="xml" articles = Entrez.read handle for article in articles 'PubmedArticle' : abstract = article 'MedlineCitation' 'Article' .get 'Abstract', {} .get 'AbstractText', "" 0 title = article 'MedlineCitation' 'Article' 'ArticleTitle' documents.append Document text=abstract, metadata={"title": title, "source": "PubMed"} return documents The secret sauce is the QueryFusionRetriever . It takes results from both Elasticsearch BM25 and Pinecone Vector and merges them using Reciprocal Rank Fusion RRF . python from llama index.vector stores.pinecone import PineconeVectorStore from llama index.retrievers.bm25 import BM25Retriever from llama index.core.retrievers import QueryFusionRetriever 1. Vector Store Pinecone vector store = PineconeVectorStore pinecone index=index vector retriever = index.as retriever similarity top k=5 2. Keyword Store BM25 via Elasticsearch Assuming documents are already indexed in Elasticsearch bm25 retriever = BM25Retriever.from defaults nodes=nodes, similarity top k=5 3. Hybrid Fusion hybrid retriever = QueryFusionRetriever vector retriever, bm25 retriever , num queries=1, Set to 1 for query expansion/rewrite mode="reciprocal rerank", use top k=True Finally, we feed the fused context into the LLM. We enforce a strict prompt template that requires the model to cite the "Source Title" from the metadata. python from llama index.core.query engine import RetrieverQueryEngine prompt template = "Context information is below.\n" "---------------------\n" "{context str}\n" "---------------------\n" "Given the context information and not prior knowledge, " "answer the query. Always cite your sources using the 'title' metadata.\n" "If the answer is not in the context, state that you do not know.\n" "Query: {query str}\n" "Answer: " query engine = RetrieverQueryEngine.from args retriever=hybrid retriever, system prompt="You are a specialized Medical Assistant." response = query engine.query "What are the latest treatments for drug-resistant hypertension?" print response Building a prototype is easy, but making it production-ready for a clinical environment involves handling PII Personally Identifiable Information , ensuring HIPAA compliance, and implementing sophisticated "Agentic RAG" loops. For more advanced patterns on architecting healthcare AI and production-ready data pipelines, I highly recommend checking out the technical deep dives at WellAlly Blog . They cover everything from optimizing embedding models for medical jargon to handling large-scale document ingestion workflows. By combining the precision of Elasticsearch with the semantic capabilities of Pinecone , and orchestrating it all via LlamaIndex , we've built a system that doesn't just "guess"—it "researches." The medical field demands high stakes. Moving from a generic LLM to a PubMed-grounded Hybrid RAG is the first step toward building AI tools that doctors can actually trust. 🩺💻 What are your thoughts? Have you struggled with hallucination in specific domains? Drop a comment below or share your favorite re-ranking strategy