Case Study: Building an Internal Knowledge Base with ShinRAG

Internal knowledge management is a universal challenge. Documentation lives in multiple places—Confluence, GitHub wikis, Slack threads, code comments, and team-specific docs. Finding the right information often means searching across multiple systems, asking colleagues, or digging through outdated documentation. This case study shows how to build an internal knowledge base assistant using ShinRAG that unifies all your documentation sources into a single, intelligent search interface.

The Problem: Scattered Knowledge

Most teams face the same knowledge management challenges:

The solution? A unified RAG-powered knowledge base that can search across all your documentation sources and provide intelligent, context-aware answers.

Building the Internal Knowledge Base

We'll walk through building an internal knowledge base assistant using ShinRAG. This system will:

• Search across multiple documentation sources
• Provide context-aware answers with source citations
• Handle technical documentation, code examples, and FAQs
• Be accessible via a simple API or chat interface

Step 1: Organize Your Knowledge Sources

First, identify and export your documentation sources. Common sources include:

Export these as structured data (JSON or CSV) or plain text files. For example:

• Export Confluence pages as markdown
• Extract README files from GitHub repositories
• Convert Google Docs to plain text
• Parse Slack thread exports for Q&A pairs

Step 2: Create Datasets in ShinRAG

Once you have your documentation exported, create datasets in ShinRAG:

[
  {
    "content": "How do I deploy to production?",
    "metadata": {
      "source": "engineering-docs",
      "category": "deployment",
      "last_updated": "2025-01-15"
    }
  },
  {
    "content": "Production deployment requires...",
    "metadata": {
      "source": "engineering-docs",
      "category": "deployment"
    }
  }
]

Create separate datasets for different knowledge domains:

• Product Documentation Dataset: User-facing documentation
• Engineering Documentation Dataset: Technical docs, architecture, code patterns
• Process Documentation Dataset: Workflows, best practices, team guides
• FAQ Dataset: Common questions and answers

Step 3: Create Specialized Agents

Create agents for each knowledge domain. Each agent connects to its corresponding dataset:

Step 4: Build the Multi-Agent Pipeline

Now, create a visual pipeline that intelligently routes queries to the right agents and synthesizes results:

This pipeline design:

• Queries all agents in parallel for maximum speed
• Synthesizes results to provide comprehensive answers
• Ranks by relevance to surface the most useful information
• Includes source citations so team members can verify information

Step 5: Integrate into Your Workflow

Once your pipeline is built, integrate it into your team's workflow:

Option 1: Slack Bot

Create a Slack bot that queries your ShinRAG pipeline:

// Slack bot integration example
app.command('/ask', async ({ command, ack, respond }) => {
  await ack();
  
  const response = await fetch('https://api.shinrag.com/pipelines/execute', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      pipelineId: 'your-pipeline-id',
      input: command.text
    })
  });
  
  const result = await response.json();
  await respond(result.output);
});

Option 2: Internal Web Interface

Build a simple web interface for your knowledge base:

• Search bar that queries the pipeline
• Display results with source citations
• Allow team members to rate answers for continuous improvement

Option 3: API Integration

Integrate directly into your existing tools:

• Add to your internal dashboard
• Embed in documentation sites
• Use in command-line tools

Advanced Features

Once you have the basic system working, consider these enhancements:

1. Confidence-Based Routing

Modify your pipeline to route queries based on confidence scores:

• If FAQ agent has high confidence (>0.8), return that answer immediately
• If all agents have low confidence, suggest asking a team member
• Route technical questions primarily to the engineering agent

2. Context-Aware Responses

Enhance your pipeline to consider context:

• Include user role (engineer, designer, PM) to tailor responses
• Consider recent documentation updates when ranking results
• Reference related documentation in responses

3. Continuous Improvement

Set up feedback loops to improve your knowledge base:

• Track which queries return low-confidence results
• Identify gaps in documentation
• Update datasets based on common questions

Best Practices

Based on building internal knowledge bases, here are key best practices:

1. Start Small, Iterate

Don't try to include everything at once. Start with your most frequently accessed documentation, then expand:

• Week 1: Add product documentation and FAQs
• Week 2: Add engineering documentation
• Week 3: Add process documentation
• Ongoing: Continuously update based on team feedback

2. Maintain Data Quality

Keep your datasets up to date:

• Set up regular exports from your documentation sources
• Remove outdated information
• Add metadata (last updated, source, category) for better retrieval

3. Encourage Team Adoption

Make it easy for your team to use:

• Integrate into tools they already use (Slack, Teams, etc.)
• Provide clear examples of how to ask questions
• Show source citations so team members can verify information
• Gather feedback and iterate based on actual usage

Real-World Impact

An internal knowledge base built with ShinRAG can transform how your team accesses information:

Getting Started

Ready to build your own internal knowledge base? Here's a quick start guide:

Conclusion

Building an internal knowledge base with ShinRAG is straightforward: export your documentation, create datasets and agents, build a pipeline, and integrate it into your workflow. The visual pipeline builder makes it easy to experiment with different architectures and iterate based on your team's needs.

The result? A unified knowledge base that helps your team find answers faster, reduces interruptions, and makes information accessible to everyone—not just those who know where to look.

Example: Building Our Own Internal Knowledge Base

To demonstrate how this works in practice, we built our own internal knowledge base using ShinRAG. Here's how we approached it:

Our Setup Process

We started by organizing our internal documentation into logical datasets:

• Engineering Documentation: Architecture decisions, code patterns, deployment guides
• Product Documentation: Feature specifications, user guides, API documentation
• Process Documentation: Team workflows, onboarding guides, best practices
• Internal FAQs: Common questions from team members about tools, processes, and policies

Building the Pipeline

Using ShinRAG's visual pipeline builder, we created a multi-agent system that:

• Queries all documentation sources in parallel for maximum speed
• Synthesizes results to provide comprehensive answers that draw from multiple sources
• Includes source citations so team members can verify information
• Routes queries intelligently based on confidence scores

What We Learned

Building our own internal knowledge base taught us several valuable lessons:

1. Start Small, Iterate

We started with just our most frequently accessed documentation, then expanded based on actual usage patterns. This approach helped us focus on what mattered most.

2. Visual Pipelines Make Iteration Fast

When we needed to adjust the pipeline—adding new agents, changing confidence thresholds, or modifying the synthesis logic—we could do it visually in minutes rather than rewriting code.

3. Multi-Agent Synthesis is Powerful

Querying multiple knowledge sources and synthesizing results gave us much better answers than any single source could provide. The synthesis node automatically prioritizes the most relevant information and removes duplicates.

4. Source Citations Build Trust

Including source citations in responses helped team members verify information and understand where answers came from. This transparency was crucial for adoption.

Integration Options

We integrated our knowledge base into our team's existing workflow through:

• Slack Integration: A simple bot that queries the pipeline and returns answers directly in Slack
• Internal Dashboard: A web interface where team members can search and browse documentation
• API Access: Direct API integration for custom tools and workflows

The visual pipeline builder made it easy to experiment with different architectures and iterate based on our team's actual needs. What started as a simple FAQ system evolved into a comprehensive knowledge base that helps our team find information faster and more reliably.