What is Graph-Based Retrieval?
Graph-Based Retrieval uses knowledge graph relationships to find relevant information through entity connections and graph traversal algorithms. Graph retrieval complements vector search with structured relationship navigation.
This RAG and knowledge systems term is currently being developed. Detailed content covering implementation approaches, best practices, technical considerations, and evaluation methods will be added soon. For immediate guidance on RAG implementation, contact Pertama Partners for advisory services.
Graph-based retrieval answers complex multi-hop questions that vector similarity search handles poorly, improving accuracy by 40-60% for relationship-dependent queries. Companies deploying knowledge graph retrieval for compliance and due diligence workflows discover relevant regulatory connections that document-level search systematically misses. The structured knowledge representation also provides explainable retrieval paths that satisfy audit requirements in regulated industries.
- Retrieves via graph traversal from seed entities.
- Captures relationships not in vector similarity.
- Enables multi-hop and relationship-based queries.
- Requires knowledge graph construction.
- Graph algorithms: PageRank, random walk, path finding.
- Combines well with vector retrieval for hybrid approaches.
- Knowledge graph construction requires significant upfront investment in entity extraction and relationship mapping; budget 2-4 months for initial graph building before retrieval deployment.
- Graph traversal depth directly affects both retrieval quality and latency; limit hop counts to 2-3 for interactive applications while allowing deeper exploration for batch analysis.
- Maintain graph freshness through automated entity and relationship extraction pipelines that update knowledge representations as source documents change.
- Construct knowledge graphs linking entities, relationships, and hierarchical taxonomies before deploying traversal algorithms for multi-hop reasoning across interconnected document repositories.
Common Questions
When should we use RAG vs. fine-tuning?
Use RAG for knowledge that changes frequently, needs citations, or is too large for context windows. Fine-tune for style, format, or behavior changes. Many production systems combine both approaches.
What are the main RAG implementation challenges?
Retrieval quality (finding right documents), chunking strategy (preserving context while fitting budgets), and evaluation (measuring end-to-end system performance). Each requires careful tuning for specific use cases.
More Questions
Evaluate retrieval quality (precision/recall), generation faithfulness (answer supported by context), answer relevance (addresses question), and end-to-end accuracy. Use frameworks like RAGAS for systematic evaluation.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
RAG (Retrieval-Augmented Generation) is a technique that enhances AI model outputs by retrieving relevant information from external knowledge sources before generating a response. RAG allows businesses to ground AI answers in their own data, reducing hallucinations and keeping responses current without retraining the model.
Naive RAG implements basic retrieve-then-generate pattern with simple chunking and single retrieval step, providing baseline RAG functionality without sophisticated optimizations. Naive RAG serves as starting point before adding advanced techniques.
Advanced RAG enhances basic RAG with query rewriting, hybrid retrieval, reranking, and iterative refinement to improve retrieval quality and answer accuracy. Advanced techniques address naive RAG limitations for production deployments.
Modular RAG decomposes RAG pipeline into interchangeable components (retriever, reranker, generator) enabling flexible composition and optimization of each stage independently. Modular design supports experimentation and gradual improvement.
Self-RAG enables models to decide when to retrieve information and critique their own outputs for factuality, improving efficiency and accuracy by avoiding unnecessary retrieval. Self-RAG adds adaptive retrieval and self-correction to standard RAG.
Need help implementing Graph-Based Retrieval?
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