Retrieval-Augmented Generation (RAG)

The ingestion layer that transforms unstructured enterprise data into a queryable vector space. This pipeline is the foundation of retrieval quality.

• Document Parsing: Extracts clean text from PDFs, HTML, and proprietary formats while preserving structural metadata like headings and tables.
• Chunking Strategy: Segments documents into semantically coherent units. Strategies include fixed-size with overlap, recursive splitting by separators, and semantic chunking that uses embedding similarity to find natural topic boundaries.
• Embedding Generation: Passes each chunk through a text embedding model such as text-embedding-3-large to produce a dense vector representation.
• Metadata Enrichment: Attaches source filename, page number, section title, and access control tags to each vector for precise attribution-aware chunking.

A poorly designed indexing pipeline is the single most common cause of retrieval failure in production RAG systems.

Combines the strengths of multiple search paradigms to maximize both recall and precision. No single retrieval method is sufficient for enterprise search.

• Dense Vector Search: Uses approximate nearest neighbor (ANN) algorithms like HNSW to find chunks semantically similar to the query, even when exact keywords differ. Excels at conceptual matching.
• Sparse Keyword Retrieval (BM25): A bag-of-words algorithm that excels at matching specific identifiers, product codes, legal citations, and rare terms that embeddings often miss.
• Metadata Filtering: Applies boolean constraints on structured metadata before vector comparison, ensuring results fall within a specific date range, document collection, or security clearance level.
• Fusion Algorithms: Reciprocal Rank Fusion (RRF) and weighted score combination merge results from dense and sparse indexes into a single ranked list.

Hybrid retrieval is the industry standard for production RAG, addressing the lexical gap where embeddings fail on exact matches.

A second-pass filtering stage that refines the initial candidate set from retrieval before it reaches the generator. Re-rankers trade latency for precision.

• Cross-Encoder Architecture: Unlike the bi-encoder used for initial retrieval, a cross-encoder processes the query and document together through full self-attention, capturing nuanced relevance signals.
• Models: Common re-rankers include Cohere Rerank, bge-reranker-v2-m3, and cross-encoders fine-tuned on MS MARCO passage ranking data.
• Score Calibration: Re-rankers output a relevance score between 0 and 1, which can be thresholded to filter out low-confidence candidates before generation.
• Latency Budgeting: Re-ranking typically adds 50-200ms to the pipeline. Effective architectures balance the number of candidates passed to the re-ranker against the total latency budget.

Without re-ranking, the generator often receives distractors in its context window, degrading answer quality and increasing hallucination risk.

The set of techniques that ensure generated text is verifiably supported by retrieved context. Grounding is the primary defense against hallucination in RAG systems.

• Grounded Decoding: Modifies token logits during generation to favor words and phrases explicitly present in the provided evidence, penalizing parametric speculation. Implemented via logit processors or constrained beam search.
• Faithfulness Metrics: Automated evaluation using Natural Language Inference (NLI) models to check if each atomic claim in the output is entailed by the source documents. Metrics include Grounded BERTScore and custom NLI-based scorers.
• Inline Citation: The generator inserts reference markers directly into the text span requiring support, enabling end-user verification. Requires the model to be fine-tuned on citation-augmented data.
• Cross-Source Verification: Requires multiple independent retrieved documents to corroborate a fact before it is presented, reducing reliance on any single potentially erroneous source.

Grounding transforms the LLM from an oracle into an auditable reasoning engine.

The preprocessing layer that bridges the gap between ambiguous user input and the structured knowledge in the vector index. Raw queries are rarely optimal for retrieval.

• Intent Classification: Routes queries to specialized indexes or agents based on the user's goal, such as summarization, comparison, or factual lookup.
• Entity Extraction: Identifies and normalizes named entities like product names, people, and locations to link them to canonical knowledge graph entries.
• Query Rewriting: Uses an LLM to expand a terse query into a more descriptive, keyword-rich form that better matches indexed document language. Also resolves anaphora in multi-turn conversations.
• HyDE (Hypothetical Document Embeddings): Generates a synthetic answer document from the query, then uses its embedding for retrieval. This captures the semantic structure of the target domain even when the query is vague.

Effective query understanding can improve retrieval recall by 30-50% without any changes to the underlying index.

The systematic logging of data origin, transformation, and consumption that creates an unbroken chain of custody from source document to generated answer. Essential for regulated industries.

• Data Lineage: Records every document's ingestion timestamp, chunking parameters, embedding model version, and index destination. Enables answering 'Why was this chunk retrieved?'
• Citation Attribution: Links each generated sentence to the exact source document, page, and chunk that supports it. Implemented via fine-tuning or constrained decoding.
• Blockchain Anchoring: Records a cryptographic hash of provenance metadata on an immutable ledger, creating a tamper-proof timestamp for compliance audits.
• Audit Logs: Capture the full retrieval and generation trace, including all retrieved candidates, re-ranker scores, and final prompt, for post-hoc debugging and regulatory review.

Provenance tracking is not optional for deployments in finance, healthcare, or legal domains where auditability is a regulatory requirement.

The process of identifying and linking specific spans of generated text to the exact source documents or data records that support them. In RAG systems, this creates a verifiable chain between the output and the retrieved context.

• Inline Citation: Direct reference markers inserted into text spans requiring evidential support
• Attribution-Aware Chunking: Preserves source metadata during document segmentation for precise citation
• Enables end-users to independently verify every factual claim

A quantitative evaluation score measuring the degree to which a generated statement is logically entailed by and consistent with the provided source context. This metric operates independently of general world knowledge, assessing only whether the output is grounded in the retrieved documents.

• Natural Language Inference (NLI): Determines entailment, contradiction, or neutrality between premise and hypothesis
• Grounded BERTScore: Computes semantic similarity specifically between generated text and source evidence
• Penalizes tokens lacking contextual support

A self-correcting technique where a language model generates an initial response, then systematically drafts and answers a series of independent verification questions to identify and correct its own factual errors before presenting the final output.

• Step 1: Generate baseline response from retrieved context
• Step 2: Draft targeted verification questions for each factual claim
• Step 3: Answer questions independently against source documents
• Step 4: Revise output to reconcile inconsistencies

A constrained text generation strategy that manipulates token probabilities during inference to favor words and phrases explicitly supported by a provided evidence document. This approach intervenes at the decoding level rather than post-hoc validation.

• Adjusts logits to penalize tokens absent from retrieved context
• Integrates with beam search and nucleus sampling strategies
• Reduces intrinsic hallucinations without additional model calls

A classification system that categorizes factual errors in language model output into distinct types to enable targeted mitigation strategies in RAG pipelines.

• Intrinsic Hallucination: Output contradicts the provided source context
• Extrinsic Hallucination: Output cannot be verified against any source
• Source-Conflict Error: Multiple retrieved documents disagree, and the model fails to reconcile them
• Temporal Hallucination: Correct fact applied to wrong time period

A grounding strategy requiring multiple independent retrieved documents to corroborate a fact before it is presented as true. This reduces reliance on any single potentially erroneous source and increases confidence in generated claims.

• Implements N-way agreement thresholds before fact inclusion
• Weighs sources by Source Reliability Score based on historical accuracy and domain authority
• Flags single-source claims with explicit uncertainty markers