While basic Retrieval Augmented Generation (RAG) is powerful for many use cases, complex knowledge scenarios often require more sophisticated approaches. Advanced RAG architectures address challenges such as multi-step reasoning, diverse information types, and specialized domain knowledge.

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Beyond Basic RAG

Standard RAG has limitations in certain scenarios:

Complex Reasoning

Questions requiring multi-step analysis or inference

Large Document Sets

Knowledge bases with millions of documents or fragments

Diverse Information Types

Heterogeneous data including structured and unstructured content

Domain-Specific Nuances

Technical fields with specialized terminology and concepts

Multi-Turn Conversations

Discussions that build on previous interactions

Dynamic Information

Content that changes frequently or requires real-time updates
Advanced RAG architectures address these challenges through specialized retrieval strategies, context processing techniques, and generation approaches.

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Advanced RAG Architectures

Prisme.ai supports several advanced RAG architectures that you can implement based on your specific needs:
A sequential approach that refines retrieval results through multiple phases.How It Works:
  1. First stage performs efficient but less precise retrieval (e.g., BM25 keyword search)
  2. Second stage applies more intensive semantic filtering on first-stage results
  3. Final stage re-ranks candidates using cross-encoders or other precise methods
  4. Only the highest quality content is passed to the LLM

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Advanced Context Processing

Beyond retrieval architectures, sophisticated methods for processing retrieved context can significantly improve response quality:

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Multi-Agent RAG Systems

For particularly complex knowledge applications, multiple specialized agents can work together:
1

Query Analysis

A specialized agent analyzes the user’s question to determine required knowledge and approach.Functions include:
  • Intent identification
  • Domain classification
  • Complexity assessment
  • Subtask identification
2

Knowledge Retrieval

Multiple specialized retrieval agents gather information from different sources.Examples include:
  • Document specialist for textual knowledge
  • Structured data agent for databases and tables
  • Knowledge graph navigator for entity relationships
  • Media analyzer for images and diagrams
3

Information Synthesis

An integration agent combines and reconciles information from various sources.Key responsibilities:
  • Resolving contradictions
  • Organizing information logically
  • Identifying information gaps
  • Creating unified context
4

Response Generation

A specialized generation agent creates the final response based on synthesized information.Focus areas:
  • Appropriate format and style
  • Clear explanation logic
  • Accurate source attribution
  • Addressing all aspects of the query
5

Self-Reflection

A critic agent reviews the response for quality and improvement opportunities.Assessment criteria:
  • Factual accuracy
  • Comprehensiveness
  • Clarity and coherence
  • Appropriate detail level
Each agent focuses on its specialized task, creating a more robust system than any single agent could provide.

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Advanced RAG Implementation with Prisme.ai

Implementing advanced RAG architectures in Prisme.ai follows a structured approach:
Using Prisme.ai’s built-in advanced configuration options.Available advanced options include:
  • Multi-stage retrieval configuration
  • Query preprocessing settings
  • Context handling parameters
  • Response generation strategies
This approach is ideal for implementing moderately advanced RAG architectures without requiring coding expertise.

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Webhook Integration for Advanced RAG

Important: The webhook functionality described below requires AI Builder and subscription to specific events. This represents a more technical implementation approach for advanced users who need complete control over the RAG process.
Prisme.ai allows you to build advanced RAG architectures by integrating external services through webhooks. This powerful feature extends the capabilities of AI Knowledge by allowing you to:
  • Implement custom processing logic
  • Integrate with specialized AI systems
  • Override various stages of the RAG pipeline
  • Create sophisticated multi-step workflows

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Webhook Subscription Events

You can subscribe to different events in the AI Knowledge lifecycle:

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Webhook Response Options

Depending on the event type, your webhook can return different responses to influence the RAG process:
Provide custom-retrieved context chunks while letting AI Knowledge handle prompt generation and LLM interaction.Response Format:
{
  "chunks": [
    {
      "value": {
        "content": "First chunk content that will be injected within LLM prompt",
        "knowledgeId": "Corresponding AIK document id"
      }
    },
    {
      "value": {
        "content": "Second chunk content...",
        "knowledgeId": "Another document id"
      }
    }
  ]
}
Ideal For:
  • Custom retrieval strategies
  • External knowledge sources
  • Specialized context processing
  • Dynamic information integration

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Setting Up Webhook Integration

To implement webhook integration for advanced RAG:
1

Create External Service

Develop your external service with the required logic to handle webhook events.Requirements:
  • HTTPS endpoint
  • Ability to process webhook requests
  • Business logic implementation
  • Response generation
2

Configure AI Builder

Set up AI Builder to enable webhook functionality.Key steps:
  • Create a new automation in AI Builder
  • Configure event subscriptions on AI Knowledge
  • Connect to your webhook endpoint
  • Set up authentication
3

Subscribe to Events

Choose which events your webhook should receive.Options include:
  • Document management events
  • Query processing events
  • Test evaluation events
4

Test Integration

Verify that your webhook receives events and responds correctly.Testing steps:
  • Monitor webhook requests
  • Validate response formats
  • Check integration behavior
  • Troubleshoot any issues

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Use Case Examples

Medical Knowledge Advisor

Challenge: Providing accurate medical information from diverse sources including research papers, clinical guidelines, and drug databases.

Advanced RAG Solution: Multi-stage retrieval with knowledge graph integration

Key Features:

  • Entity recognition for medical terms
  • Relationship tracking between conditions, treatments, and medications
  • Source prioritization based on evidence quality
  • Self-reflective validation for factual accuracy

Legal Research Assistant

Challenge: Navigating complex legal documents, precedents, and statutes with precise citation and reasoning.

Advanced RAG Solution: Recursive retrieval with contextual routing

Key Features:

  • Hierarchical decomposition of legal questions
  • Jurisdiction-aware retrieval pathways
  • Citation tracking and verification
  • Temporal reasoning about law changes

Technical Support Advisor

Challenge: Troubleshooting complex technical issues spanning multiple products, versions, and systems.

Advanced RAG Solution: Multi-agent RAG with self-reflection

Key Features:

  • Problem classification and decomposition
  • Product-specific knowledge agents
  • Step-by-step solution synthesis
  • Verification against known issues database

Financial Analyst

Challenge: Analyzing financial data from reports, market trends, and news to provide investment insights.

Advanced RAG Solution: Hypothetical document embeddings with structured data integration

Key Features:

  • Financial query expansion and reformulation
  • Integration of numerical data analysis
  • Time-sensitive information prioritization
  • Data visualization for complex insights

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Advanced RAG Best Practices

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Next Steps

RAG Configuration

Learn about the core configuration options for RAG

Tools Integration

Extend your agents with specialized capabilities

Agent Testing

Validate advanced RAG implementations

AI Builder Documentation

Learn more about AI Builder for advanced implementations