Hallucination Mitigation

An architectural framework that grounds a model's response by dynamically retrieving relevant external documents from a vector database before generation. Instead of relying solely on parametric knowledge, the model is provided with a specific context window of source material.

• Mechanism: Query embedding → Dense vector search → Top-k document injection into prompt
• Key Benefit: Decouples factual knowledge from model weights, allowing real-time updates
• Example: A support bot retrieves the latest product manual to answer a configuration question, rather than hallucinating a deprecated setting

A generation technique that forces the model's output to strictly adhere to a predefined schema, grammar, or regular expression. This ensures structural validity by masking invalid tokens at each step of the sampling process.

• Mechanism: Logit manipulation to set probability of invalid tokens to negative infinity
• Key Benefit: Guarantees syntactically correct JSON, SQL, or domain-specific language output
• Example: Forcing a financial extraction model to output only valid ISO 4217 currency codes, preventing it from inventing 'XYZ' as a currency

The practice of anchoring model outputs to deterministic, structured data stored in an enterprise knowledge graph. Instead of generating free text, the model queries a graph of verified entities and relationships.

• Mechanism: NLU to SPARQL/Cypher translation → Graph traversal → Response synthesis from triples
• Key Benefit: Provides mathematically provable provenance for every asserted fact
• Example: A pharmaceutical AI answers drug interaction queries by traversing a curated medical ontology rather than relying on memorized training data

A prompting strategy where the model generates an initial response and then systematically fact-checks its own output through a series of independent verification questions.

• Mechanism: Draft response → Decompose into atomic claims → Execute verification queries → Correct inconsistencies
• Key Benefit: Reduces hallucination by treating the model as a generator and a separate critic
• Example: An LLM drafts a biography, then asks itself 'What is the exact birth date per Wikipedia?' for each person mentioned, correcting errors before final output

Embedding explicit markers of certainty, source quality, and data freshness within content to guide an AI model's trust assessment during retrieval and synthesis.

• Mechanism: Metadata tags indicating confidence scores, last-verified timestamps, and source authority
• Key Benefit: Allows the model to weight information appropriately or abstain from answering
• Example: A medical database marks entries as 'Evidence Level: Meta-Analysis' vs. 'Evidence Level: Case Report', enabling the AI to prioritize high-confidence sources

A response synthesis strategy that strictly constrains the model's output to be derived from and supported by a provided set of source documents. The model is prohibited from introducing external knowledge.

• Mechanism: Instruction tuning to cite spans and refuse out-of-context generation
• Key Benefit: Creates a verifiable audit trail from every output sentence back to a source document
• Example: A legal AI summarizes a contract and is forced to attribute each clause summary to a specific paragraph number, making fabrication immediately detectable

An architectural framework that grounds a language model's response by dynamically retrieving relevant external documents from a vector database before generation. RAG is the primary defense against hallucination in enterprise settings.

• Mechanism: Encodes a user query into a dense vector, performs a similarity search against a knowledge base, and injects the top-k results into the prompt as factual grounding.
• Key Benefit: Decouples the model's parametric memory from the source of truth, allowing for real-time updates without retraining.
• Risk: If the retriever fetches irrelevant documents, the generator may still hallucinate based on incorrect context.

A generation technique that forces the model's output to strictly adhere to a predefined schema, grammar, or regular expression to ensure structural validity. This prevents the model from generating tokens that violate a known format.

• Implementation: Uses a logit mask at each generation step to set the probability of invalid tokens to negative infinity.
• Use Case: Guarantees that an LLM outputs valid JSON, a specific ontology class, or a controlled vocabulary term.
• Limitation: Enforces syntactic correctness but does not guarantee semantic accuracy; the model can still output a valid-looking but factually wrong statement.

Methods for reinforcing the truthfulness of content through verifiable data, structured references, and contradiction minimization. This is a proactive content-engineering approach to mitigation.

• Citation Injection: Embedding explicit, verifiable source URLs or document IDs directly into the generated text.
• Attribution Tracing: Using NLI models to verify that every declarative statement in a generated response is entailed by a provided source document.
• Contradiction Detection: Running a secondary model to identify logical inconsistencies between generated sentences before presenting output to the user.

Embedding explicit markers of certainty, source quality, and data freshness within content to guide an AI model's trust assessment. This helps the model understand when to express uncertainty.

• Token Probability Analysis: Examining the softmax output for the selected token; a low probability indicates the model is guessing.
• Verbalized Uncertainty: Prompting the model to output a confidence score or to state 'I don't know' when supporting evidence is below a threshold.
• Semantic Entropy: Measuring the variance in meaning across multiple sampled generations for the same prompt; high entropy correlates with a high likelihood of hallucination.

The process of populating and aligning enterprise data with public knowledge bases like Wikidata and Google's Knowledge Graph to establish entity identity and authority. A deterministic knowledge graph acts as a hallucination-free oracle.

• Entity Linking: Resolving ambiguous mentions in text to a unique, canonical node in the graph.
• Triple Validation: Checking generated subject-predicate-object triples against a curated graph to filter out factual errors.
• Advantage: Provides a rigid, logical structure that prevents the statistical 'smoothing' of facts that occurs in dense vector embeddings.

A response synthesis strategy that strictly constrains the model's output to be derived from and supported by a provided set of source documents. This is the 'generation' half of the RAG architecture.

• Instruction Tuning: Fine-tuning a model to refuse to answer if the context lacks sufficient information.
• Post-Hoc Verification: Using a Natural Language Inference model to classify if the hypothesis (the generated claim) is entailed by the premise (the retrieved text).
• Chain-of-Verification: A multi-step process where the model drafts a response, generates verification questions, answers them against a knowledge base, and corrects inconsistencies.