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# Large Language Models ❤️ Knowledge Graphs

Fine-tuning LLMs is an expensive trap. Graph RAG is the solution. Learn to build explainable AI agents by grounding models in verifiable, highly connected knowledge graphs.

- **Speakers:** [Michael Hunger](https://www.wearedevelopers.com/@michael-hunger)
- **Event:** World Congress 2024
- **Published:** August 22, 2024
- **Duration:** 29:11
- **URL:** https://www.wearedevelopers.com/videos/1154-large-language-models-knowledge-graphs

## Summary

Large language models (LLMs) offer unprecedented natural language capabilities but inherently struggle with factual accuracy, frequently hallucinating when queried about recent events or proprietary enterprise data. While fine-tuning is often considered the default solution, it is extremely costly and lacks the segregation necessary to enforce data access controls. Instead, grounding LLMs using Retrieval-Augmented Generation (RAG) ensures generated answers rely exclusively on curated database context rather than unpredictable pre-trained knowledge.

Elevating standard RAG architectures, knowledge graphs transform unstructured enterprise data into a highly connected, explainable format known as Graph RAG. By leveraging the LLM's language skills to extract concrete entities and relationships from raw text—such as PDFs or conference agendas—engineering teams can automatically construct a rich foundational graph in databases like Neo4j. This programmatic "digital twin" of organizational knowledge perfectly merges the analytical precision of relational graph entities with the semantic flexibility of vector embeddings.

During retrieval, Graph RAG applications combine initial vector searches to locate relevant text chunks with deep graph traversal to fetch interconnected context. This hybrid querying approach drastically curtails hallucinations by collapsing the LLM's token prediction solely onto verifiable, multi-hop facts. Beyond accuracy, knowledge graphs introduce essential traceability to generative AI systems, enabling developers to visually map exactly which internal documents and nodes influenced an output. Supported by frameworks like LangChain, LlamaIndex, and Spring AI, this methodology empowers teams to build context-aware, highly accurate, and fully explainable AI agents.

**Keywords:** llm hallucinations, retrieval-augmented generation, knowledge graphs, graph RAG, neo4j databases, vector search integration, entity and relationship extraction, langchain framework, llamaindex, enterprise data grounding, generative ai explainability, structured output generation, context window grounding, fine-tuning alternatives

## Chapters

1. **Introduction to generative AI and knowledge graphs** (00:02) — Combining language models with graph databases yields powerful approaches to organizing and processing enterprise data.
1. **Overcoming hallucination challenges in large language models** (01:47) — The tendency of base models to hallucinate necessitates anchoring them to specific organizational knowledge bases.
1. **Comparing fine-tuning to database retrieval and grounding** (03:14) — Grounding language models via accessible database context offers superior security and efficiency compared to full fine-tuning.
1. **Using retrieval-augmented generation for situational context** (04:42) — Passing relevant document fragments into response prompts restricts the search space and improves accurate completions.
1. **Structuring enterprise data utilizing knowledge graph databases** (06:26) — Viewing large-scale corporate data as interconnected nodes facilitates intuitive querying and effective digital twin modeling.
1. **Modeling public forum data with graph relationships** (08:05) — Mapping forum questions and users illustrates the practical scale and pattern-matching abilities of graph schemas.
1. **Extracting graph entities from unstructured text automatically** (10:20) — Leveraging base language intelligence extracts reliable structured semantic entities from unstructured text documents automatically.
1. **Demonstrating document ingestion and knowledge graph visualization** (12:51) — Integrating a document processing pipeline demonstrates how raw schedules become navigable nodes and vector embeddings.
1. **Combining vector search with structured graph traversal** (16:40) — Employing dual search methods enables applications to pinpoint entry facts and dynamically follow related patterns.
1. **Querying knowledge graphs and vector indexes via Python** (19:11) — Orchestrating operations via scripting integrates database drivers and models for explicit user query resolution.
1. **Adding explainability and topic clustering to applications** (24:19) — Tracking source links and applying mathematical clustering ensures reliable transparency and restricts untruthful model generations.
1. **Exploring educational resources for developing graph applications** (26:21) — Reviewing available platform tutorials and code repositories prepares engineering teams for successful architectural implementations.
1. **Validating extraction quality and directing graph creation** (27:21) — Crafting exact entity schemas and comparing automated outputs to human validation guarantees high data quality.

## Related Moments

- [Unlocking generative AI capabilities using knowledge graphs](https://www.wearedevelopers.com/videos/100318-context-graphs-for-explainable-decision-aware-ai-agents) (from "Context Graphs for Explainable, Decision-Aware AI Agents")
- [Enhancing language models with graph retrieval augmented generation](https://www.wearedevelopers.com/videos/1311-graphs-and-rags-everywhere-but-what-are-they-andreas-kollegger-neo4j) (from "Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j")
- [Enhancing language models with retrieval-augmented generation](https://www.wearedevelopers.com/videos/844-enter-the-brave-new-world-of-genai-with-vector-search) (from "Enter the Brave New World of GenAI with Vector Search")
- [Combining knowledge graphs with LLMs for reliability](https://www.wearedevelopers.com/videos/1160-give-your-llms-a-left-brain) (from "Give Your LLMs a Left Brain")
- [Overcoming language model challenges using retrieval-augmented generation](https://www.wearedevelopers.com/videos/1249-building-blocks-of-rag-from-understanding-to-implementation) (from "Building Blocks of RAG: From Understanding to Implementation")
- [Mitigating artificial intelligence hallucinations with constraints and context](https://www.wearedevelopers.com/videos/994-mastering-ai-driven-problem-solving-in-engineering-with-observability) (from "Mastering AI-Driven Problem Solving in Engineering with Observability")

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- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
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