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Knowledge BaseRAG & Semantic Search

RAG & Semantic Search

Understanding how LoopReply uses Retrieval-Augmented Generation to power knowledge-based responses.

What is RAG?

RAG (Retrieval-Augmented Generation) combines information retrieval with AI text generation:

  1. Retrieval: Find relevant content from your knowledge base
  2. Augmentation: Add that content to the AI’s context
  3. Generation: AI produces a response using the retrieved information

This approach lets your bot answer questions based on your specific content, not just general knowledge.

How It Works

1. Content Processing

When you add content to the knowledge base:

Your Content → Chunking → Embedding → Vector Storage
  • Chunking: Content is split into smaller pieces
  • Embedding: Each chunk is converted to a numerical vector
  • Storage: Vectors are stored for fast similarity search

2. Query Processing

When a user asks a question:

User Question → Embedding → Similarity Search → Top K Chunks
  • Embedding: The question is converted to a vector
  • Similarity Search: Find chunks with similar vectors
  • Top K: Return the most relevant chunks

3. Response Generation

The AI generates a response:

System Prompt + Retrieved Chunks + User Question → AI Response

The retrieved chunks provide context that grounds the AI’s response in your actual content.

Unlike keyword search, semantic search understands meaning:

QueryKeyword SearchSemantic Search
”How do I cancel?”Matches “cancel”Finds “terminate subscription”, “end membership"
"pricing”Matches “pricing”Finds “costs”, “fees”, “how much"
"doesn’t work”Matches exactlyFinds troubleshooting content

Benefits

  • Natural language: Users don’t need exact keywords
  • Synonym handling: Different words, same meaning
  • Context awareness: Understands intent, not just words

Best Practices

Quality Over Quantity

  • Fewer, high-quality sources beat many low-quality ones
  • Remove duplicate and contradictory content
  • Keep content current and accurate

Optimize Content Structure

  • Use clear headings
  • Write explicit answers, not vague references
  • Include common question variations

Test Retrieval

When testing in Bot Studio, examine retrieved chunks:

  • Are the right chunks being found?
  • Are there better chunks that aren’t being retrieved?

Handle Edge Cases

  • Provide fallback responses for low-confidence retrievals
  • Train your bot to say “I don’t know” when appropriate
  • Offer human escalation for complex questions

Limitations

What RAG Can’t Do

  • Real-time data: Knowledge base is static between updates
  • Reasoning: Can retrieve facts but complex reasoning is limited
  • Structured queries: Not a replacement for database queries

Mitigation

  • Regular content updates
  • Combine with workflows for structured interactions
  • Use API calls for live data when needed
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