Grounded Context & Citation

is citation & grounded context is same??

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Good question — they’re related but not the same, and interviewers often use them loosely, which causes confusion.

Short answer

❌ Citation ≠ Grounded Context

✅ Citation supports grounding, but grounding is broader.

1. What is Grounded Context?

Grounded context means the AI response is anchored to trusted, known data, not hallucinated.

The model is constrained to:

• Enterprise documents

• Policies

• Contracts

• Databases

• Knowledge bases

• Retrieved search results (RAG)

👉 Goal: Accuracy, relevance, and reduced hallucination

Example

“Based on your 2024 supplier contract (Clause 7.2), termination requires 60 days’ notice.”

Here, the answer is grounded because it came from a real contract.

2. What is a Citation?

A citation is proof of grounding — it shows where the information came from.

It can be:

• Document name

• Section / clause number

• Page number

• URL / document ID

👉 Goal: Transparency and trust

Example

“Termination requires 60 days’ notice (Supplier_Contract_v3.pdf, Clause 7.2).”

That clause reference is the citation.

3. Relationship Between the Two

Aspect	Grounded Context	Citation
What it is	Data the model is restricted to	Evidence reference
Purpose	Prevent hallucinations	Build trust & auditability
Required for accuracy	✅ Yes	❌ Not strictly
Required for compliance	Sometimes	Often
Visible to user	Not always	Usually

Key insight:

A response can be grounded without showing citations, but a citation implies the response was grounded.

4. In Enterprise / Procurement / BFSI Context

You’ll often say:

“We ground GenAI responses using enterprise-approved data sources and provide clause-level citations for auditability and compliance.”

That sentence shows:

• You understand AI safety

• You understand regulatory expectations

• You know how to operationalize GenAI

5.

“Grounded context ensures the model answers only from trusted enterprise data, while citations provide traceability and auditability by showing exactly which document or clause the answer came from.”

Sample Codebase

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Below is a simple, ready reference codebase that demonstrates Grounded Context + Citations using:

• Java + Spring Boot

• Spring AI

• Azure OpenAI

• PostgreSQL + pgvector

• RAG (Retrieval-Augmented Generation)

I’ll keep it clean, minimal, and explainable so you can walk through it confidently.

1️⃣ Architecture Flow (What You Explain Verbally)

User Question
   ↓
Query Embedding
   ↓
Vector Search (pgvector)
   ↓
Retrieve Relevant Documents (Grounded Context)
   ↓
Prompt with Context + Citation Metadata
   ↓
LLM Answer (with Citations)

Key idea:

LLM is NOT answering from memory — only from retrieved enterprise documents.

2️⃣ Database Schema (Grounded Data + Citation Metadata)

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE procurement_docs (
    id UUID PRIMARY KEY,
    content TEXT,
    source_doc VARCHAR(255),
    section VARCHAR(100),
    embedding VECTOR(1536)
);

Example row:

content	source_doc	section
"Termination requires 60 days notice..."	Supplier_Contract_v3.pdf	Clause 7.2

➡️ source_doc + section = citation

3️⃣ Document Ingestion (Embedding with Metadata)

@Autowired
private EmbeddingClient embeddingClient;

@Autowired
private JdbcTemplate jdbcTemplate;

public void ingestDocument(String content, String source, String section) {

    List<Double> embedding = embeddingClient.embed(content);

    jdbcTemplate.update("""
        INSERT INTO procurement_docs (id, content, source_doc, section, embedding)
        VALUES (?, ?, ?, ?, ?)
        """,
        UUID.randomUUID(),
        content,
        source,
        section,
        embedding.toArray()
    );
}

🔹 Grounding starts here🔹 Every chunk carries traceability

4️⃣ Vector Search (Grounded Context Retrieval)

public List<Map<String, Object>> retrieveContext(String questionEmbedding) {

    String sql = """
        SELECT content, source_doc, section
        FROM procurement_docs
        ORDER BY embedding <-> ?::vector
        LIMIT 3
        """;

    return jdbcTemplate.queryForList(sql, questionEmbedding);
}

Only enterprise-approved documents are retrieved.

5️⃣ Prompt Construction (Grounded + Citation-Aware)

public String buildPrompt(
        String userQuestion,
        List<Map<String, Object>> docs) {

    StringBuilder context = new StringBuilder();

    for (Map<String, Object> doc : docs) {
        context.append("""
        Content:
        %s
        Source: %s, %s

        """.formatted(
            doc.get("content"),
            doc.get("source_doc"),
            doc.get("section")
        ));
    }

    return """
    Answer the question ONLY using the context below.
    If the answer is not present, say "Information not found."

    Context:
    %s

    Question:
    %s

    Include citations in your answer.
    """.formatted(context, userQuestion);
}

This is hard grounding:

• No context → No answer

• Citations mandatory

6️⃣ LLM Call (Azure OpenAI via Spring AI)

@Autowired
private ChatClient chatClient;

public String answerQuestion(String prompt) {
    return chatClient.call(prompt);
}

7️⃣ Final User Response (Grounded + Cited)

User Question:

“What is the supplier contract termination notice period?”

LLM Response:

“The supplier contract requires a termination notice period of 60 days.(Supplier_Contract_v3.pdf, Clause 7.2)”

✅ Grounded✅ Cited✅ Auditable✅ Compliance-ready

8️⃣Summary

“We implement GenAI using a RAG-based grounded architecture. User queries are converted to embeddings and matched against enterprise-approved documents stored in pgvector. The retrieved content forms the only context passed to the LLM, preventing hallucination. Each document chunk carries metadata like document name and clause number, which we surface as citations in the response to ensure traceability, auditability, and regulatory compliance.”