Retrieval-Augmented Generation (RAG)

The retrieval mechanism uses dense vector embeddings to find semantically relevant documents, not just keyword matches. When a query is received, it is converted into a high-dimensional vector. A cosine similarity search is then performed against a vector database to identify the closest matching chunks.

• Uses models like text-embedding-3-large for query vectorization
• Employs approximate nearest neighbor (ANN) algorithms for speed
• Retrieves top-k results based on similarity threshold

Retrieved documents are assembled into a structured prompt alongside the user's original query. This process, known as in-context learning, provides the LLM with factual grounding without fine-tuning. The assembly must respect the model's token allocation limits.

• Documents are ordered by relevance score
• Source metadata and citation signals are appended
• System prompts enforce grounding behavior

Enterprise RAG systems combine dense vector search with sparse keyword retrieval (like BM25) to handle both conceptual and exact-match queries. This hybrid approach ensures robust performance across diverse query types.

• Dense retrieval captures semantic meaning
• Sparse retrieval excels at specific identifiers (e.g., product codes)
• Results are fused using reciprocal rank fusion

Source documents are segmented into discrete, self-contained content chunks optimized for retrieval. Effective chunking strategies balance semantic completeness with retrieval precision.

• Fixed-size chunking: splits by token count with overlap
• Semantic chunking: splits based on sentence boundaries and topic shifts
• Metadata like document title and section is preserved per chunk

RAG inherently mitigates hallucination by constraining generation to retrieved context. Advanced implementations include explicit citation signals that map generated claims back to source documents.

• Inline citations link statements to provenance
• Confidence scores can be assigned to retrieved passages
• Enables data provenance auditing for compliance

The external data source can be a vector database, a knowledge graph, or a hybrid of both. Vector stores handle unstructured text, while knowledge graphs provide deterministic, entity-rich facts.

• Pinecone, Weaviate, and Milvus are common vector stores
• Knowledge graphs inject structured entity relationships
• Real-time indexing keeps the knowledge base current

A numerical representation of data as a point in a high-dimensional space where semantic similarity corresponds to geometric proximity. In RAG, documents are converted into embeddings and stored in a vector database. When a query is embedded, a cosine similarity search retrieves the nearest neighbors—the most semantically relevant chunks—to inject into the LLM's context window.

The process of segmenting long-form content into discrete, self-contained semantic blocks optimized for vector database indexing. Effective chunking strategies balance:

• Size: Small enough for precise retrieval, large enough to retain context
• Overlap: Maintaining semantic continuity between adjacent chunks
• Integrity: Preserving complete sentences and logical units Poor chunking leads to fragmented context and degraded RAG output quality.

A set of techniques used to reduce the frequency of factually incorrect outputs from LLMs. RAG is the primary architectural defense, anchoring generation in retrieved evidence rather than parametric knowledge alone. Other complementary methods include:

• Grounding: Forcing the model to cite specific source passages
• Confidence calibration: Explicitly signaling uncertainty
• Contradiction detection: Cross-referencing generated claims against retrieved facts

A technical indicator within content that allows AI models to correctly attribute sourced information, establishing provenance and authority. In RAG systems, citation signals enable the generator to produce verifiable references. Effective citation engineering includes:

• Explicit source attribution in retrieved chunks
• Persistent document identifiers linked to knowledge base entries
• Structured metadata marking author, date, and confidence level

A structured database storing entities and their interrelationships, used to enhance RAG retrieval with factual context beyond simple semantic matching. While vector search excels at similarity, knowledge graphs provide deterministic precision—retrieving exact relationships like 'Paris is the capital of France.' Hybrid RAG architectures combine vector search for breadth with graph traversal for depth.

The point at which an LLM's context window is filled to capacity, potentially causing the model to ignore or forget information at the beginning of the input. RAG systems must carefully manage token allocation to balance:

• Retrieved document chunks
• System instructions and prompt templates
• Conversation history
• Generated output space Overstuffing degrades both retrieval relevance and generation quality.