# Retrieval-Augmented Generation (RAG): Stop Your AI from Hallucinating

> Source: <https://dev.to/mzunain/retrieval-augmented-generation-rag-stop-your-ai-from-hallucinating-17e8>
> Published: 2026-07-11 06:00:00+00:00

You ask your AI: "What's our company's revenue for Q3 2026?"

You get a confident, detailed answer. Total fabrication.

This is hallucination. The model makes up answers when it doesn't have information.

RAG solves this by giving your AI access to real data before answering.

RAG = Retrieval-Augmented Generation

**Traditional AI**: Question → Model → Answer (no context)

**RAG**: Question → Search knowledge base → Retrieve relevant documents → Model reads documents → Answer

It's like giving your AI access to reference materials before an exam.

``` python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import PDFLoader

loader = PDFLoader("company_docs.pdf")
docs = loader.load()

embeddings = OpenAIEmbeddings()
vector_store = FAISS.from_documents(docs, embeddings)
retriever = vector_store.as_retriever()
python
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

qa = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=retriever
)

result = qa.run("What's our Q3 revenue?")
print(result)  # Now grounded in real data!
python
from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separators=["\n\n", "\n", " ", ""]
)

chunks = splitter.split_documents(docs)
vector_store = FAISS.from_documents(chunks, embeddings)
# Use similarity score threshold
retriever = vector_store.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"score_threshold": 0.7}
)
```

**Customer Support**: AI references your knowledge base while answering

**Legal Discovery**: Search contract database, cite sources

**Medical**: AI consults latest research papers

**Finance**: Real-time market data access

**HR**: Company policy retrieval

```
# Combine semantic + keyword search
results = vector_store.similarity_search(query, k=10)
keyword_results = bm25_search(query)
combined = merge_results(results, keyword_results)
# Improve question before retrieval
original_query = "stuff about money"
rewritten = llm.predict(
    f"Rewrite this for a database search: {original_query}"
)
# Returns: "financial statements Q3 2026"
# Retrieve many, rank few
retrieved = retriever.get_relevant_documents(query)  # Get 100
ranked = rerank(retrieved, query, top_k=5)  # Keep 5 best
context = "\n".join([d.page_content for d in ranked])
```

**Mistake 1**: Poor chunking → Broken context

**Solution**: Experiment with chunk size

**Mistake 2**: Outdated documents → Stale answers

**Solution**: Implement refresh schedule

**Mistake 3**: No deduplication → Waste tokens

**Solution**: Remove duplicate documents

**Mistake 4**: Bad embeddings → Poor retrieval

**Solution**: Use domain-specific embedding models

```
# Track hallucination rate
hallucinations = 0
for question, expected_answer in test_cases:
    response = qa.run(question)
    if not verify_against_docs(response):
        hallucinations += 1

hallucination_rate = hallucinations / len(test_cases)
print(f"Hallucination rate: {hallucination_rate*100}%")
```

In 2026, RAG is becoming standard for:

Companies not using RAG will face:

Take your most important company document and:

You'll never trust hallucinating AI again.

**Are you using RAG? What's your document source?**