3.2 Level 2: Building Robust and Fact-Based Systems

Key Points to Cover:

The Problem: Hallucinations and Lack of Domain Knowledge

• Understanding Hallucinations
• Why LLMs generate false information
• Examples of hallucinations in code and technical contexts
• Impact on reliability and trust

• When LLMs "don't know" but answer anyway

• Domain Knowledge Limitations

• Training data cutoff dates
• Missing proprietary/internal knowledge
• Industry-specific terminology and practices
• Real-time information needs

The Solution: Retrieval-Augmented Generation (RAG)

• What is RAG?
• Combining retrieval with generation
• External knowledge base + LLM reasoning
• Architecture overview: Query → Retrieve → Augment → Generate

sequenceDiagram
    participant User as User 🧑
    participant LLM as LLM 🧠
    participant DB as Vector Database 📚
    participant Docs as Your Docs 📄

    Note over User,LLM: 😱 Without RAG
    User->>LLM: "What's our API key rotation policy?"
    LLM->>User: "I don't have that info...
*makes up answer* 🤷"

    Note over User,Docs: ✨ With RAG Magic!
    User->>LLM: "What's our API key rotation policy?"
    LLM->>DB: Search for relevant docs
    DB->>Docs: Find matching content
    Docs->>DB: "Found! Section 4.2 of Security Policy"
    DB->>LLM: Return actual policy text
    LLM->>User: "According to your security docs,
keys rotate every 90 days... ✅"
    User->>User: 🎉 Accurate answer!

• RAG Components
• Vector databases (Pinecone, Weaviate, Chroma, etc.)
• Embedding models
• Retrieval mechanisms

• Context injection

• Implementation Steps

• Document chunking and preprocessing
• Creating embeddings
• Storing in vector database
• Similarity search
• Context augmentation

• LLM query with context

• Use Cases for RAG

• Internal documentation Q&A
• Code repository assistants
• Customer support systems
• Technical knowledge bases

The "Ground Truth": Knowledge Graphs & Ontologies

• What are Knowledge Graphs?
• Structured representation of knowledge
• Nodes (entities) and edges (relationships)
• Semantic meaning and connections
• Examples: Google Knowledge Graph, enterprise KGs

graph TD
    subgraph "Knowledge Graph: Software Team 👥"
        Alice[Alice
👨‍💻 Developer]
        Bob[Bob
👨‍💻 Developer]
        Carol[Carol
👩‍💼 Manager]
        Auth[Auth Service
🔐]
        Payment[Payment API
💰]
        Python[Python 3.11
🐍]
        FastAPI[FastAPI
⚡]
    end

    Alice -->|maintains| Auth
    Bob -->|maintains| Payment
    Carol -->|manages| Alice
    Carol -->|manages| Bob
    Auth -->|written_in| Python
    Auth -->|uses_framework| FastAPI
    Payment -->|depends_on| Auth
    Payment -->|written_in| Python

    style Alice fill:#a8daff
    style Bob fill:#a8daff
    style Carol fill:#ffd6a8
    style Auth fill:#c4ffc4
    style Payment fill:#c4ffc4

• Why Knowledge Graphs Matter
• Explicit relationships and hierarchies
• Reasoning capabilities
• Data consistency and validation

• Complex query support

• Ontologies Explained

• Formal definitions of concepts and relationships
• Domain modeling
• Standards (OWL, RDF, SPARQL)
• Industry ontologies

Graph RAG: Next-Level Integration

• How LLMs Learn to Query Graph Databases
• Text-to-SPARQL or Text-to-Cypher translation
• LLM as query interface
• Combining structured and unstructured data
• Multi-hop reasoning

flowchart TD
    Q["User Question:
Who maintains services
that depend on Auth?"] --> LLM{LLM}

    LLM -->|Translates to| Cypher["Cypher Query:
MATCH path"]

    Cypher -->|Query| KG[Knowledge Graph]
    KG -->|Results| Result[Bob]
    Result -->|Format| LLM
    LLM -->|Natural Language| Answer["Bob maintains the Payment API
which depends on Auth Service
He is managed by Carol"]

    style Q fill:#ffe1e1
    style Answer fill:#e1ffe1
    style LLM fill:#e1e1ff
    style KG fill:#ffffcc

• Architecture of Graph RAG Systems
• Query understanding
• Graph database querying (Neo4j, Neptune, etc.)
• Result contextualization

• Natural language response generation

• Advantages Over Traditional RAG

• Better handling of complex relationships
• More precise answers
• Explainable results
• Reduced hallucinations

graph TD
    Q["Question: How many developers
work on Python services
managed by Carol?"]

    subgraph Standard[Standard LLM]
        S1[Guesses based on training] --> S2[Maybe 2-3? I think...]
    end

    subgraph RAG[Traditional RAG]
        R1[Searches text docs] --> R2[Finds Carol manages Alice and Bob] 
        R2 --> R3[Probably 2 but not certain]
    end

    subgraph GraphRAG[Graph RAG]
        G1[Queries Knowledge Graph] 
        G1 --> G2[MATCH query finds paths]
        G2 --> G3[Gets exact connections]
        G3 --> G4[Exactly 2 - Alice maintains Auth
Bob maintains Payment
Both use Python]
    end

    Q --> S1
    Q --> R1
    Q --> G1

    style Standard fill:#ffcccc
    style RAG fill:#ffffcc
    style GraphRAG fill:#ccffcc
    style S2 fill:#ff9999
    style R3 fill:#ffeb99
    style G4 fill:#99ff99

Live Demo

• Comparison Demonstration
• Query 1: Standard LLM (shows potential hallucination)
• Query 2: Same question with RAG system

• Query 3: Same question with Knowledge Graph integration

• Show Differences

• Accuracy improvements
• Source attribution
• Handling of complex multi-step queries
• Factual grounding

Implementation Considerations

• Technical Stack Options
• Graph databases: Neo4j, ArangoDB, Amazon Neptune
• Vector stores: Pinecone, Qdrant, Milvus
• Frameworks: LangChain, LlamaIndex

• LLM APIs

• Data Preparation

• Building the knowledge graph
• Entity extraction and linking
• Relationship mapping

• Maintenance and updates

• Performance Optimization

• Caching strategies
• Index optimization
• Latency considerations
• Scaling for production