Hallucination Mitigation

Forces the model to generate tokens that strictly adhere to a predefined JSON Schema or Context-Free Grammar (CFG). By using a Finite State Machine (FSM) during token generation, the probability of any token that would break the structural contract is set to zero. This prevents the model from inventing new fields, omitting required keys, or generating unparseable syntax, effectively eliminating structural hallucinations.

Grounds the model's output in a trusted, external knowledge base rather than relying solely on its parametric memory. The pipeline retrieves relevant documents via semantic search and injects them into the prompt as authoritative context. This shifts the task from 'recalling facts' to 'summarizing provided text', drastically reducing the model's opportunity to confabulate entities or statistics.

Applies a penalty or boost to specific tokens before the final sampling step. To mitigate entity hallucinations, a logit bias can suppress the generation of high-risk tokens (e.g., fictional names) or enforce the selection of tokens from a verified entity list. Token masking dynamically sets the probability of invalid tokens to zero, physically preventing the model from generating out-of-schema or unverified text.

A multi-step prompting strategy where the model first generates a baseline response, then plans a series of independent verification questions, answers them factually, and finally revises the original response based on the verified facts. This structured reasoning loop helps the model self-correct factual inconsistencies before presenting the final output to the user.

Setting the temperature parameter to zero eliminates randomness in token selection, forcing the model to always choose the highest probability token. While this doesn't guarantee factual accuracy, it ensures deterministic output—the same input will always produce the same output. This reproducibility is critical for debugging, testing, and ensuring that a hallucination, once fixed, does not reappear randomly.

Requires the model to explicitly link every factual claim back to a specific source document or data record. By enforcing a structured output format that includes mandatory citation fields (e.g., source_url, document_id), the system creates a verifiable audit trail. If a claim cannot be cited, it is either suppressed or flagged as low-confidence, preventing unattributed hallucinations from reaching the end-user.

The process of anchoring a model's generated text to verifiable source documents. This is the primary architectural defense against hallucination.

• Retrieval-Augmented Generation (RAG) is the most common implementation, fetching relevant chunks from a vector database to serve as the 'ground truth' context for the model.
• Citation Attribution: Requires the model to explicitly link each factual claim back to a specific source chunk, enabling user verification.
• Mechanism: Shifts the model's task from pure recall to summarization and synthesis of provided text, drastically reducing the statistical chance of confabulation.

Constraining the model's generation to a predefined schema acts as a powerful syntactic and semantic guardrail against hallucination.

• Schema Validation: By forcing output into a JSON Schema or Pydantic model, invalid data types or fabricated fields are immediately rejected by the parser.
• Grammar-Constrained Generation: Techniques like Outlines or GBNF Grammar use a Finite State Machine (FSM) to mask tokens that would break the format, preventing the model from generating free-form, unverifiable prose.
• Benefit: Transforms a fuzzy text-generation problem into a deterministic data-extraction task, making errors programmatically detectable.

Using a curated, deterministic knowledge base to validate the factual consistency of generated outputs.

• Entity Resolution: Before generation, entities mentioned in a query are linked to a canonical node in the Enterprise Knowledge Graph.
• Relationship Validation: Generated triples (subject-predicate-object) are checked against the graph's edges. A claim that contradicts the graph is flagged as a hallucination.
• Advantage: Provides a high-precision, logic-based verification layer that complements the probabilistic retrieval of vector search, ensuring strict adherence to known facts.

Prompting strategies that force the model to critique its own output and make its reasoning auditable.

• Chain-of-Thought (CoT) Structuring: Requires the model to output explicit reasoning steps in a parseable format before the final answer, making logical leaps visible.
• Self-Correction: A multi-step prompt where the model first generates an answer, then acts as a critic to identify inconsistencies or unsupported claims, and finally revises the output.
• Recursive Error Correction: An agentic loop where the model iteratively checks its work against retrieved sources until a confidence threshold is met, embodying a 'think step-by-step and verify' paradigm.

Eliminating the randomness in the token sampling process to ensure consistent, reproducible outputs.

• Temperature Zero: Setting the temperature parameter to 0 forces the model to always select the single most probable token. This disables the creative 'sampling' that often leads to confabulation.
• Deterministic Output: For a given input and seed, the output is perfectly reproducible, which is critical for debugging and auditing factual errors.
• Use Case: Essential for data extraction, summarization of known facts, and any task where factual precision outweighs stylistic variation.

Directly manipulating the model's output probabilities to suppress tokens that are likely to introduce hallucinations.

• Logit Bias: A parameter that adds a penalty or boost to the score of specific tokens before sampling. You can suppress tokens like 'possibly' or 'maybe' to force more assertive, grounded language.
• Token Masking: Dynamically sets the probability of invalid tokens to negative infinity, physically preventing their generation. This is the core mechanism behind Guided Decoding.
• Application: Can be used to prevent the model from generating specific names, dates, or unverified claims by blacklisting tokens associated with known hallucination patterns.