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Overview#
The RAG domain provides a complete pipeline for document ingestion, semantic search, and LLM-powered question answering with citations. It enables investigators to query case evidence using natural language and receive grounded, citation-backed answers from an AI assistant.
Key Features#
- Document ingestion with semantic chunking, token counting, and metadata preservation
- Hybrid search combining vector similarity and keyword matching with result fusion
- LLM-powered question answering with context building and optimized prompts
- Citation extraction linking answers to specific source documents, pages, and excerpts
- Hallucination detection with grounding verification for answer factuality
- User feedback collection with thumbs up/down ratings for quality improvement
- Case-scoped search to focus queries on specific investigation evidence
- Re-ranking with LLM-based relevance scoring for improved result accuracy
- Response caching for repeated queries with configurable cache settings
- Support for multiple document types including witness statements, reports, transcripts, and forensic results
Use Cases#
- Querying case evidence in natural language to find relevant information with cited sources
- Ingesting investigation documents for semantic search and AI-powered analysis
- Verifying answer grounding to ensure AI responses are factually supported by evidence
- Collecting analyst feedback to improve search relevance and answer quality over time
Integration#
The RAG domain connects with language model operations, evidence management, case management, analytical tools, and search infrastructure.
Last Reviewed: 2026-02-05