Modern RAG-powered chatbot architecture for enterprise data governance

Introduction

Data governance has become a critical challenge for modern enterprises. With regulatory requirements like GDPR, CCPA, and industry-specific compliance mandates, organizations need intelligent systems that can answer governance questions instantly, provide policy guidance, and ensure consistent interpretation of complex regulations.

Retrieval-Augmented Generation (RAG) chatbots solve this problem by combining the conversational capabilities of Large Language Models (LLMs) with the precision of enterprise knowledge retrieval. Instead of relying solely on an LLM’s training data, RAG systems retrieve relevant governance documents, policies, and regulations in real-time, then use the LLM to generate accurate, contextual responses.

In this comprehensive guide, you’ll learn how to build a production-ready RAG chatbot for data governance, including:

– Understanding RAG architecture for governance use cases – Implementing document ingestion and vectorization pipelines – Building intelligent retrieval systems with metadata filtering – Creating conversational interfaces with audit trails – Ensuring compliance and explainability in AI responses

By the end of this tutorial, you’ll have a working RAG chatbot that can answer questions about your organization’s data policies, privacy regulations, and compliance requirements.

Why RAG for Data Governance?

Traditional chatbots struggle with data governance because:

1. Constantly changing regulations: Compliance requirements evolve frequently, making static training data obsolete 2. Domain-specific language: Governance documents contain specialized terminology and legal language 3. Citation requirements: Governance decisions need traceable sources and audit trails 4. Multi-source knowledge: Organizations have policies spread across documents, databases, and systems

RAG architecture addresses these challenges by:

– Dynamic knowledge retrieval: Always accesses the latest versions of governance documents – Source attribution: Provides citations for every response, enabling audit trails – Controlled responses: Limits answers to verified organizational knowledge, reducing hallucinations – Easy updates: New policies are added without retraining models

RAG Architecture for Data Governance

A production-ready RAG system for data governance consists of five core components:

graph TD
    A[User Query] --> B[Query Processing]
    B --> C[Vector Search]
    C --> D[Document Retrieval]
    D --> E[Context Assembly]
    E --> F[LLM Generation]
    F --> G[Response with Citations]

H[Document Store] --> C
    I[Vector Database] --> C
    J[Metadata Filters] --> C

style A fill:#F5A623,stroke:#C77D1A,color:#fff
    style F fill:#4A90E2,stroke:#2E5C8A,color:#fff
    style G fill:#27AE60,stroke:#1E8449,color:#fff
    style C fill:#9B59B6,stroke:#6C3483,color:#fff

Component Breakdown

1. Query Processing: Analyzes user intent, extracts entities (e.g., “GDPR Article 17”), applies security filters 2. Vector Search: Converts query to embeddings, performs semantic similarity search across governance documents 3. Document Retrieval: Fetches relevant policy sections, regulations, and compliance guidelines 4. Context Assembly: Combines retrieved documents with system prompts and conversation history 5. LLM Generation: Produces natural language responses grounded in retrieved evidence

Complete RAG architecture showing document ingestion, retrieval, and generation pipelines

Building the Document Ingestion Pipeline

The first step is creating a robust pipeline to ingest, process, and vectorize governance documents.

Document Processing Strategy

graph LR
    A[Raw Documents] --> B[Document Parser]
    B --> C[Text Chunking]
    C --> D[Metadata Extraction]
    D --> E[Embedding Generation]
    E --> F[Vector Database]

G[PDF/Word/HTML] --> B
    H[Policies/Regulations] --> B

style A fill:#F5A623,stroke:#C77D1A,color:#fff
    style E fill:#4A90E2,stroke:#2E5C8A,color:#fff
    style F fill:#27AE60,stroke:#1E8449,color:#fff

Implementation with LangChain

Here’s a production-ready document ingestion pipeline:

from langchain.document_loaders import (
    PyPDFLoader,
    UnstructuredWordDocumentLoader,
    TextLoader
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
import pinecone
from datetime import datetime
from typing import List, Dict
import hashlib

class GovernanceDocumentPipeline:
    """
    Production pipeline for ingesting data governance documents
    into a RAG system with metadata tracking and versioning.
    """

def __init__(
        self,
        pinecone_api_key: str,
        pinecone_environment: str,
        index_name: str = "governance-docs"
    ):
        # Initialize Pinecone vector database
        pinecone.init(
            api_key=pinecone_api_key,
            environment=pinecone_environment
        )

self.embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
        self.index_name = index_name

# Create index if it doesn't exist
        if index_name not in pinecone.list_indexes():
            pinecone.create_index(
                name=index_name,
                dimension=3072,  # text-embedding-3-large dimension
                metric="cosine"
            )

self.vectorstore = Pinecone.from_existing_index(
            index_name=index_name,
            embedding=self.embeddings
        )

def ingest_document(
        self,
        file_path: str,
        document_type: str,
        regulation: str = None,
        effective_date: str = None,
        department: str = None
    ) -> Dict:
        """
        Ingest a single governance document with metadata.

Args:
            file_path: Path to document file
            document_type: Type (policy, regulation, guideline, etc.)
            regulation: Related regulation (GDPR, CCPA, HIPAA, etc.)
            effective_date: When policy became effective
            department: Owning department

Returns:
            Dictionary with ingestion statistics
        """
        # Load document based on file type
        if file_path.endswith('.pdf'):
            loader = PyPDFLoader(file_path)
        elif file_path.endswith('.docx'):
            loader = UnstructuredWordDocumentLoader(file_path)
        else:
            loader = TextLoader(file_path)

documents = loader.load()

# Split documents into chunks
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200,
            separators=["\n\n", "\n", ". ", " ", ""]
        )

chunks = text_splitter.split_documents(documents)

# Add metadata to each chunk
        for i, chunk in enumerate(chunks):
            chunk.metadata.update({
                "document_type": document_type,
                "regulation": regulation or "general",
                "effective_date": effective_date or datetime.now().isoformat(),
                "department": department or "compliance",
                "chunk_index": i,
                "total_chunks": len(chunks),
                "source_file": file_path,
                "ingestion_date": datetime.now().isoformat(),
                "document_hash": self._generate_hash(chunk.page_content)
            })

# Store in vector database
        self.vectorstore.add_documents(chunks)

return {
            "status": "success",
            "file": file_path,
            "chunks_created": len(chunks),
            "document_type": document_type,
            "regulation": regulation
        }

def _generate_hash(self, content: str) -> str:
        """Generate unique hash for deduplication."""
        return hashlib.sha256(content.encode()).hexdigest()[:16]

def batch_ingest(self, documents: List[Dict]) -> List[Dict]:
        """
        Ingest multiple documents in batch.

Args:
            documents: List of document configurations

Returns:
            List of ingestion results
        """
        results = []
        for doc_config in documents:
            try:
                result = self.ingest_document(**doc_config)
                results.append(result)
            except Exception as e:
                results.append({
                    "status": "error",
                    "file": doc_config.get("file_path"),
                    "error": str(e)
                })

return results

Example usage
if __name__ == "__main__":
    pipeline = GovernanceDocumentPipeline(
        pinecone_api_key="your-pinecone-key",
        pinecone_environment="us-west1-gcp"
    )

# Ingest GDPR privacy policy
    result = pipeline.ingest_document(
        file_path="policies/gdpr-privacy-policy.pdf",
        document_type="privacy_policy",
        regulation="GDPR",
        effective_date="2024-01-15",
        department="legal"
    )

print(f"Ingested {result['chunks_created']} chunks from {result['file']}")

Key Design Decisions

1. Chunk Size: 1000 characters with 200-character overlap balances context preservation and retrieval precision 2. Metadata Richness: Extensive metadata enables sophisticated filtering (by regulation, department, date) 3. Document Hashing: Prevents duplicate ingestion and enables version tracking 4. Error Handling: Batch processing continues even if individual documents fail

Automated document processing pipeline with quality checks and metadata extraction

Implementing Intelligent Retrieval

Effective retrieval is critical for governance chatbots. Simple semantic search isn’t enough—you need metadata filtering, reranking, and query enhancement.

Advanced Retrieval Strategy

graph TD
    A[User Question] --> B[Query Analysis]
    B --> C{Query Type}
    C -->|Policy Question| D[Filter: Policies]
    C -->|Regulation Question| E[Filter: Regulations]
    C -->|Procedure Question| F[Filter: Procedures]

D --> G[Vector Search]
    E --> G
    F --> G

G --> H[Initial Results]
    H --> I[Reranking]
    I --> J[Top K Documents]
    J --> K[Context Window]

style A fill:#F5A623,stroke:#C77D1A,color:#fff
    style G fill:#9B59B6,stroke:#6C3483,color:#fff
    style I fill:#4A90E2,stroke:#2E5C8A,color:#fff
    style K fill:#27AE60,stroke:#1E8449,color:#fff

Production Retrieval Implementation

from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from typing import List, Dict, Optional
import re

class GovernanceRetriever:
    """
    Advanced retrieval system for governance documents with
    metadata filtering, query enhancement, and reranking.
    """

def __init__(self, vectorstore, cohere_api_key: str):
        self.vectorstore = vectorstore
        self.llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0)

# Query enhancement chain
        self.query_enhancer = self._create_query_enhancer()

# Reranker for improving relevance
        self.reranker = CohereRerank(
            cohere_api_key=cohere_api_key,
            top_n=5
        )

def _create_query_enhancer(self) -> LLMChain:
        """Create chain to enhance user queries with governance context."""
        template = """You are a data governance expert. Enhance this user question to improve document retrieval.

User Question: {question}

Enhanced Query Instructions:
1. Expand abbreviations (GDPR → General Data Protection Regulation)
2. Add relevant synonyms (data subject → individual, user, customer)
3. Include regulation context if mentioned
4. Preserve original intent

Enhanced Query:"""

prompt = PromptTemplate(
            input_variables=["question"],
            template=template
        )

return LLMChain(llm=self.llm, prompt=prompt)

def retrieve(
        self,
        question: str,
        filters: Optional[Dict] = None,
        top_k: int = 5
    ) -> List[Dict]:
        """
        Retrieve relevant governance documents with filtering and reranking.

Args:
            question: User's governance question
            filters: Metadata filters (regulation, department, date range)
            top_k: Number of documents to return

Returns:
            List of relevant document chunks with metadata
        """
        # Enhance query for better retrieval
        enhanced_query = self.query_enhancer.run(question=question)

# Apply metadata filters
        search_kwargs = {"k": top_k * 3}  # Retrieve more for reranking

if filters:
            search_kwargs["filter"] = self._build_filter(filters)

# Initial retrieval
        initial_docs = self.vectorstore.similarity_search(
            query=enhanced_query,
            **search_kwargs
        )

# Rerank results
        compressed_docs = self.reranker.compress_documents(
            documents=initial_docs,
            query=question  # Use original question for reranking
        )

# Format results with metadata
        results = []
        for doc in compressed_docs[:top_k]:
            results.append({
                "content": doc.page_content,
                "metadata": doc.metadata,
                "relevance_score": getattr(doc, 'relevance_score', None)
            })

return results

def _build_filter(self, filters: Dict) -> Dict:
        """Convert user filters to vector database filter format."""
        db_filter = {}

if "regulation" in filters:
            db_filter["regulation"] = {"$in": filters["regulation"]}

if "document_type" in filters:
            db_filter["document_type"] = filters["document_type"]

if "department" in filters:
            db_filter["department"] = filters["department"]

if "effective_after" in filters:
            db_filter["effective_date"] = {"$gte": filters["effective_after"]}

return db_filter

def retrieve_with_citations(
        self,
        question: str,
        filters: Optional[Dict] = None
    ) -> Dict:
        """
        Retrieve documents and format with proper citations.

Returns:
            Dictionary with retrieved content and formatted citations
        """
        docs = self.retrieve(question, filters)

context_parts = []
        citations = []

for i, doc in enumerate(docs, 1):
            # Add numbered reference to context
            context_parts.append(
                f"[{i}] {doc['content']}\n"
                f"Source: {doc['metadata'].get('source_file', 'Unknown')}\n"
            )

# Build citation
            citations.append({
                "number": i,
                "source": doc['metadata'].get('source_file'),
                "regulation": doc['metadata'].get('regulation'),
                "effective_date": doc['metadata'].get('effective_date'),
                "excerpt": doc['content'][:200] + "..."
            })

return {
            "context": "\n".join(context_parts),
            "citations": citations
        }

Example usage
if __name__ == "__main__":
    retriever = GovernanceRetriever(
        vectorstore=vectorstore,
        cohere_api_key="your-cohere-key"
    )

# Retrieve with filters
    results = retriever.retrieve(
        question="What are the data subject rights under GDPR?",
        filters={
            "regulation": ["GDPR"],
            "document_type": "regulation"
        }
    )

for doc in results:
        print(f"Relevance: {doc['relevance_score']}")
        print(f"Content: {doc['content'][:200]}...")
        print(f"Source: {doc['metadata']['source_file']}\n")

Multi-stage retrieval with query enhancement, filtering, and reranking for optimal accuracy

Building the Conversational Interface

Now we’ll create the chatbot interface that combines retrieval with LLM generation, including conversation memory and audit logging.

Conversation Flow Architecture

graph TD
    A[User Message] --> B[Security Check]
    B --> C[Intent Classification]
    C --> D{Intent Type}
    D -->|Factual Query| E[RAG Pipeline]
    D -->|Clarification| F[Conversation Memory]
    D -->|Follow-up| G[Context Assembly]

E --> H[Generate Response]
    F --> H
    G --> H

H --> I[Citation Formatting]
    I --> J[Audit Logging]
    J --> K[User Response]

style A fill:#F5A623,stroke:#C77D1A,color:#fff
    style E fill:#9B59B6,stroke:#6C3483,color:#fff
    style H fill:#4A90E2,stroke:#2E5C8A,color:#fff
    style K fill:#27AE60,stroke:#1E8449,color:#fff

Complete Chatbot Implementation

from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
import logging
import json
from datetime import datetime

class DataGovernanceChatbot:
    """
    Production RAG chatbot for data governance with
    conversation memory, citations, and audit trails.
    """

def __init__(self, retriever: GovernanceRetriever):
        self.retriever = retriever
        self.llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0)

# Conversation memory
        self.memory = ConversationBufferMemory(
            memory_key="chat_history",
            return_messages=True,
            output_key="answer"
        )

# Audit logger
        self.audit_logger = self._setup_audit_logger()

# System prompt
        self.system_prompt = self._create_system_prompt()

# Build conversational chain
        self.chain = self._create_chain()

def _create_system_prompt(self) -> str:
        """Create system prompt for governance chatbot."""
        return """You are an expert data governance assistant for enterprise compliance.

Your role:
- Answer questions about data policies, regulations, and compliance requirements
- Provide accurate information based ONLY on retrieved documents
- Always cite sources using [1], [2] notation
- If information isn't in retrieved documents, say "I don't have that information in the current governance documents"
- Be precise with regulatory language—don't paraphrase legal text
- Highlight important compliance obligations clearly

Important guidelines:
- Never make up policy information
- Always reference the specific document and section
- For ambiguous questions, ask for clarification
- Note when policies might conflict and recommend consulting legal team
- Include effective dates when discussing policy changes

Retrieved Documents:
{context}

Provide clear, accurate responses based on the above documents."""

def _create_chain(self) -> ConversationalRetrievalChain:
        """Create conversational retrieval chain."""
        return ConversationalRetrievalChain.from_llm(
            llm=self.llm,
            retriever=self.retriever.vectorstore.as_retriever(
                search_kwargs={"k": 5}
            ),
            memory=self.memory,
            return_source_documents=True,
            verbose=False
        )

def _setup_audit_logger(self) -> logging.Logger:
        """Setup audit trail logger."""
        logger = logging.getLogger("governance_chatbot")
        logger.setLevel(logging.INFO)

handler = logging.FileHandler("governance_audit.log")
        formatter = logging.Formatter(
            '%(asctime)s | %(levelname)s | %(message)s'
        )
        handler.setFormatter(formatter)
        logger.addHandler(handler)

return logger

def chat(
        self,
        question: str,
        user_id: str,
        filters: Optional[Dict] = None
    ) -> Dict:
        """
        Process user question and generate response with citations.

Args:
            question: User's governance question
            user_id: User identifier for audit trail
            filters: Optional metadata filters

Returns:
            Response dictionary with answer, citations, and metadata
        """
        # Log query for audit
        self.audit_logger.info(
            f"USER: {user_id} | QUERY: {question} | FILTERS: {filters}"
        )

# Retrieve relevant documents
        retrieval_result = self.retriever.retrieve_with_citations(
            question=question,
            filters=filters
        )

# Generate response
        response = self.chain({
            "question": question,
            "context": retrieval_result["context"]
        })

# Format final response
        formatted_response = {
            "answer": response["answer"],
            "citations": retrieval_result["citations"],
            "source_documents": len(retrieval_result["citations"]),
            "timestamp": datetime.now().isoformat(),
            "filters_applied": filters or {}
        }

# Log response for audit
        self.audit_logger.info(
            f"USER: {user_id} | RESPONSE: {response['answer'][:100]}... | "
            f"SOURCES: {len(retrieval_result['citations'])}"
        )

return formatted_response

def clear_history(self):
        """Clear conversation memory."""
        self.memory.clear()

def export_conversation(self, user_id: str) -> str:
        """Export conversation for compliance records."""
        messages = self.memory.load_memory_variables({})

export_data = {
            "user_id": user_id,
            "export_date": datetime.now().isoformat(),
            "conversation": messages
        }

return json.dumps(export_data, indent=2)

Example usage
if __name__ == "__main__":
    # Initialize chatbot
    chatbot = DataGovernanceChatbot(retriever=retriever)

# Interactive session
    user_id = "legal_team_member_123"

response = chatbot.chat(
        question="What are the retention requirements for customer data under GDPR?",
        user_id=user_id,
        filters={"regulation": ["GDPR"]}
    )

print(f"Answer: {response['answer']}\n")
    print("Citations:")
    for citation in response['citations']:
        print(f"[{citation['number']}] {citation['source']} ({citation['regulation']})")
        print(f"    {citation['excerpt']}\n")

# Follow-up question
    response2 = chatbot.chat(
        question="What happens if we violate those requirements?",
        user_id=user_id,
        filters={"regulation": ["GDPR"]}
    )

print(f"\nFollow-up Answer: {response2['answer']}")

Production chatbot interface with conversation memory, citations, and audit logging

Production Deployment Considerations

Deploying a governance chatbot requires careful attention to security, compliance, and monitoring.

Security and Compliance Architecture

graph TD
    A[User Request] --> B[Authentication]
    B --> C[Authorization Check]
    C --> D{Access Level}
    D -->|Authorized| E[Role-Based Filtering]
    D -->|Unauthorized| F[Access Denied]

E --> G[RAG Pipeline]
    G --> H[Response Generation]
    H --> I[PII Redaction]
    I --> J[Audit Logging]
    J --> K[Encrypted Response]

L[Compliance Monitor] --> J
    M[Security Logs] --> J

style B fill:#E74C3C,stroke:#C0392B,color:#fff
    style C fill:#E74C3C,stroke:#C0392B,color:#fff
    style I fill:#F39C12,stroke:#D68910,color:#fff
    style J fill:#9B59B6,stroke:#6C3483,color:#fff
    style K fill:#27AE60,stroke:#1E8449,color:#fff

Key Production Requirements

1. Authentication & Authorization – Integrate with enterprise SSO (SAML, OAuth2) – Implement role-based access control (RBAC) – Filter documents based on user permissions

2. Data Security – Encrypt data at rest and in transit (TLS 1.3) – Implement PII redaction in responses – Use secure vector database configurations – Regular security audits and penetration testing

3. Audit & Compliance – Log all queries and responses with timestamps – Track document access patterns – Generate compliance reports for auditors – Maintain immutable audit trails

4. Monitoring & Observability – Track retrieval accuracy metrics – Monitor response latency (target: <2 seconds) - Alert on high error rates or security events - Dashboard for usage analytics

5. Scalability – Horizontal scaling for API servers – Distributed vector database (Pinecone, Weaviate) – Caching layer for frequently asked questions – Load balancing and failover mechanisms

Example Monitoring Setup

from prometheus_client import Counter, Histogram, start_http_server
import time

class MonitoredGovernanceChatbot(DataGovernanceChatbot):
    """Chatbot with Prometheus monitoring."""

def __init__(self, args, *kwargs):
        super().__init__(args, *kwargs)

# Metrics
        self.query_counter = Counter(
            'governance_queries_total',
            'Total number of governance queries',
            ['user_id', 'regulation']
        )

self.response_latency = Histogram(
            'governance_response_seconds',
            'Response generation latency',
            buckets=[0.5, 1.0, 2.0, 5.0, 10.0]
        )

self.retrieval_quality = Histogram(
            'governance_retrieval_docs',
            'Number of documents retrieved',
            buckets=[1, 3, 5, 10, 20]
        )

def chat(self, question: str, user_id: str, filters: Optional[Dict] = None) -> Dict:
        """Chat with monitoring."""
        start_time = time.time()

# Track query
        regulation = filters.get('regulation', ['general'])[0] if filters else 'general'
        self.query_counter.labels(user_id=user_id, regulation=regulation).inc()

# Execute chat
        response = super().chat(question, user_id, filters)

# Track metrics
        latency = time.time() - start_time
        self.response_latency.observe(latency)
        self.retrieval_quality.observe(response['source_documents'])

return response

Start Prometheus metrics server
start_http_server(8000)

Best Practices and Common Pitfalls

Document Chunking Strategy

Do: – Use semantic chunking that preserves policy sections – Include document context in metadata (section titles, article numbers) – Test chunk sizes with actual governance documents (500-1500 chars) Don’t: – Split mid-sentence or mid-paragraph arbitrarily – Use fixed character limits without overlap – Ignore document structure (headers, lists, tables)

Retrieval Optimization

Do: – Implement hybrid search (vector + keyword) for precise terminology – Use metadata filtering to narrow search space – Rerank results with cross-encoder models (Cohere, Jina) – Cache frequently asked questions Don’t: – Rely solely on semantic similarity for legal text – Ignore recency—prioritize recent policy versions – Return too many documents (>10 context overwhelms LLM)

Response Generation

Do: – Use low temperature (0-0.3) for factual consistency – Instruct LLM to cite sources explicitly – Implement confidence thresholds—flag uncertain responses – Include disclaimers for complex legal questions Don’t: – Allow hallucinations—always ground in retrieved documents – Paraphrase legal language loosely – Hide sources or make citations optional – Generate responses without retrieved context

Audit and Compliance

Do: – Log every query, response, and document accessed – Include user identifiers and timestamps – Store conversation exports for legal review – Implement data retention policies aligned with regulations Don’t: – Store PII in logs without encryption – Delete audit trails prematurely – Skip access control on sensitive governance documents

Conclusion

Building a RAG-powered chatbot for data governance transforms how organizations manage compliance knowledge. By combining intelligent document retrieval with conversational AI, you create a system that:

– Answers governance questions instantly with accurate, cited responses – Adapts to regulatory changes without model retraining – Provides audit trails for compliance verification – Democratizes governance knowledge across the organization

The architecture we’ve built—document ingestion, intelligent retrieval, conversational interface, and production monitoring—provides a solid foundation for enterprise deployment.

Key Takeaways

1. RAG architecture is ideal for governance because it combines LLM flexibility with document precision 2. Metadata filtering is critical—regulation type, department, and effective dates enable targeted retrieval 3. Citations and audit trails aren’t optional—they’re requirements for governance applications 4. Production deployment demands security, monitoring, and compliance safeguards 5. Continuous improvement through retrieval metrics and user feedback enhances accuracy over time

Next Steps

Ready to deploy your governance chatbot? Consider these enhancements:

– Multi-modal support: Process tables, charts, and diagrams from policy documents – Multilingual capabilities: Support global regulations in multiple languages – Feedback loops: Let users rate responses to improve retrieval quality – Integration: Connect with JIRA, Confluence, or compliance management systems – Advanced analytics: Identify knowledge gaps and frequently misunderstood policies

Start with a focused use case—perhaps GDPR compliance or privacy policy questions—validate with your legal team, then expand to broader governance domains.

Further Reading: – LangChain RAG Documentation – Vector Database Comparison for Enterprise – Prompt Engineering for Legal AI

—

Have questions about implementing RAG chatbots? Share your governance use case in the comments below!