Building a Trustworthy RAG System: Make AI Answers Traceable with Source Citations

A good AI system shouldn’t just answer questions; it should show where the answers come from.

1. Why AI Needs “Citations”

If you’ve ever asked an AI assistant something like,

“Summarize this project report.”
or
“What safety standard is mentioned in chapter 3 of our manual?”

You’ve probably noticed the answer sounds confident but gives no clue where it came from.

In enterprise or research environments, this isn’t just a user experience issue, but a trust problem.

Auditors need traceability.
Engineers need to verify sources.
Managers need explainability.

So, how can we make AI answers verifiable and auditable? That’s where RAG comes in.

2. What Is RAG?

RAG stands for Retrieval-Augmented Generation, and it basically means,

Let your language model “look up” real documents before it speaks.

Instead of guessing from memory, the model retrieves factual content first, and then uses it to generate the final response.

Retrieval: The system searches a document library for the most relevant text chunks.
Augmentation: It attaches those chunks to the user’s question.
Generation: The model uses both the question and retrieved data to write an answer.

For Example

Question: “What does the PDCA cycle stand for?”
RAG retrieves: “PDCA stands for Plan, Do, Check, Act — a continuous improvement method. (source: local://docs/quality_manual#p3)”
Model responds: “According to Quality Manual (page 3), PDCA includes four stages: Plan, Do, Check, and Act.”

That’s a transparent, verifiable answer, an AI that shows its work.

3. The Goal: A Unified `rag.query()` Interface

We’ll build a simple function that works with any search engine.

def query(
    corpus: str,
    q: str,
    k: int = 6,
    require_citations: bool = True
) -> list[dict]:
"""Return [{'text': '...', 'sourceUrl': 'local://...', 'score': 0.82}, ...]"""

Invoke a call,

query("local://docs", "What is the PDCA cycle?")

Output, each snippet includes both text and sourceUrl, making every answer traceable.

[
  {
    "text": "PDCA cycle stands for Plan, Do, Check, and Act, used for continuous improvement...",
    "sourceUrl": "local://docs/quality_manual#p3",
    "score": 0.89
  },
  {
    "text": "The PDCA model promotes iterative learning and process standardization...",
    "sourceUrl": "local://docs/process_guide#p12",
    "score": 0.81
  }
]

4. From Documents to Traceable Answers

Load Data Sources

The system can connect to:

Local folders (/docs) containing PDFs, Markdown, or text files
Cloud sources like SharePoint or Google Drive

files = load_documents("/docs")

Chunk the Text

Long documents are split into smaller, overlapping chunks, each tagged with a source reference.

chunks = [
  {"text": "PDCA is a four-stage model for continuous improvement...",
   "sourceUrl": "local://docs/quality_manual#p3"},
  {"text": "Developed by W....",
   "sourceUrl": "local://docs/process_history#p7"}
]

Vectorize the Chunks

We convert each chunk into an embedding, a numerical vector that captures its meaning.

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
vectors = model.encode([c["text"] for c in chunks])

These embeddings are stored in a vector database for fast similarity search.

Retrieve Relevant Segments

When a user asks a question,

query("local://docs", "Explain the PDCA model")

The system:

Embeds the question,
Computes similarity against stored vectors,
Returns the top-k matching chunks, each with its sourceUrl and score.

[
  {"text": "PDCA stands for Plan, Do, Check, Act...",
   "sourceUrl": "local://docs/quality_manual#p3",
   "score": 0.89}
]

5. About FAISS, PGVector, Elastic, and Vector Cloud Services

All these names refer to vector search engines, tools for storing and querying embeddings.

Engine	Description	When to Use
FAISS	A fast, open-source library from Facebook AI for local vector search.	Local or prototype projects
PGVector	A PostgreSQL extension that stores vectors directly in the database.	Enterprise apps needing SQL + vectors
Elastic (with vector plugin)	Adds vector search to Elasticsearch, ideal for document-based data.	Large text or log systems
Vector Cloud Services	Hosted options like Pinecone, Milvus Cloud, or Azure Cognitive Search.	Scalable cloud deployments

Our rag.query() interface hides these differences, so switching engines doesn’t break your business logic.

6. Unified Design: Replace the Engine, Keep the Logic

We can use a standard retriever interface. When we swap FAISS for PGVector or a cloud API, the query() function stays the same.

class Retriever:
    def retrieve(self, query: str, k: int): ...

class FAISSRetriever(Retriever): ...
class PGVectorRetriever(Retriever): ...

def query(corpus, q, k=6, require_citations=True):
    retriever = get_retriever(corpus)
    return retriever.retrieve(q, k)

7. Conclusion: Make Every Answer Verifiable

1st-gen AI focused on fluency — “Can it talk?”
2nd-gen AI focused on accuracy — “Does it know?”
3rd-gen AI (RAG) focuses on accountability — “Can it prove it?”

By implementing a unified, citation-enabled rag.query() interface, you build an AI system that is transparent, auditable, and easy to maintain, no matter how the underlying search engine evolves.

The future of AI isn’t just about answers. It will be about answers with evidence.