Retrieval & Knowledge Integration
RAG patterns, knowledge grounding, and external data integration.
Chunking for Context Quality
How you split documents into chunks for retrieval determines not just what gets found but how well the model can reason over and generate from retrieved context. Prerequisites: rag-prompt-design.md, 06-context-engineering-fundamentals/conversation-history-management.md
Citation and Attribution Prompting
Designing citation instructions that models follow consistently transforms RAG outputs from unverifiable text into auditable, trust-building responses with traceable claims. Prerequisites: grounding-and-faithfulness.md, rag-prompt-design.md
Dynamic Context Augmentation
Rather than retrieving all context upfront, dynamic augmentation makes runtime decisions to fetch additional information based on confidence levels, identified gaps, and intermediate reasoning results. Prerequisites: retrieval-query-design.md, reranking-and-context-selection.md, grounding-and-faithfulness.md
Grounding and Faithfulness
Grounding techniques instruct the model to generate claims only from provided context, reducing RAG hallucination rates from 20-30% to 5-10% through structured prompting patterns. Prerequisites: rag-prompt-design.md, reranking-and-context-selection.md
Hybrid Retrieval Context Patterns
Combining dense (embedding), sparse (keyword), and structured (SQL/graph) retrieval methods through fusion produces more robust context than any single method alone. Prerequisites: retrieval-query-design.md, reranking-and-context-selection.md
Knowledge Conflicts and Resolution
When retrieved documents contradict each other or conflict with the model’s training data, explicit conflict resolution strategies prevent the model from silently choosing one version or hallucinating a compromise. Prerequisites: grounding-and-faithfulness.md, rag-prompt-design.md
RAG Prompt Design
The prompt template that wraps retrieved documents and user queries determines whether a RAG system produces faithful, well-cited answers or hallucinates despite having the right information. Prerequisites: 06-context-engineering-fundamentals/conversation-history-management.md, 04-system-prompts-and-instruction-design/system-prompt-anatomy.md
Reranking and Context Selection
Initial retrieval casts a wide net returning 10-50 candidates, but only 3-5 chunks fit the context window — reranking and selection determine which make the cut. Prerequisites: retrieval-query-design.md, chunking-for-context-quality.md
Retrieval Query Design
The user’s raw question is rarely the optimal retrieval query — transforming it through rewriting, decomposition, and hypothetical document generation dramatically improves what gets retrieved. Prerequisites: rag-prompt-design.md, chunking-for-context-quality.md