AI/ML
AI Hallucination
What AI hallucination is and why it matters
Definition
AI hallucination occurs when a language model generates text that is factually incorrect, fabricated, or unsupported by its training data or provided context. Hallucinations happen because LLMs are trained to produce statistically likely text, not verified truth. Managing hallucination through techniques like RAG, structured prompts, output validation, and confidence scoring is critical for building reliable AI applications.
How it works
LLMs do not look up facts in a database. They predict the most likely next word based on patterns learned during training. This means they can confidently state things that are completely wrong -- citing papers that do not exist, inventing statistics, or attributing quotes to the wrong person. The output reads as authoritative even when the content is fabricated.
Hallucination risk varies by task. Summarizing a document the model can see is low risk - the facts are right there in the context. Answering open-ended factual questions from memory is high risk. Generating creative content is not really hallucination at all - it is the desired behavior. The key is matching the task to the appropriate level of factual verification.
Mitigation strategies include RAG (grounding responses in retrieved documents), chain-of-thought prompting (making the model show its reasoning), output validation (checking claims against structured data), confidence scoring (having the model rate its own certainty), and human review for high-stakes outputs. No single technique eliminates hallucination entirely, but layering them reduces it to manageable levels.
How 1Raft uses AI Hallucination
We design every AI feature with hallucination mitigation appropriate to the stakes. In a healthcare project where incorrect information could affect patient care, we layer RAG with citation requirements, structured output validation, and human review for edge cases. In lower-stakes applications like content suggestions, we use RAG and confidence thresholds. We never ship an AI feature that presents generated text as verified fact without a grounding mechanism.
Related terms
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that combines a language model with a searchable knowledge base. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents first, then generates answers grounded in that specific data.
AI/ML
Prompt Engineering
Prompt engineering is the practice of crafting and optimizing the instructions given to a language model to get consistent, high-quality outputs. It is the most accessible and cost-effective way to improve AI application behavior without modifying the underlying model.
AI/ML
Fine-Tuning
Fine-tuning is the process of training a pre-trained AI model on a smaller, domain-specific dataset to adapt its behavior for a particular task. It modifies the model's internal weights so it performs better on your specific use case without training from scratch.
AI/ML
Token (AI Context)
A token is the basic unit of text that a language model processes. Words, parts of words, and punctuation are all broken into tokens. Token counts determine model costs, context window limits, and response length constraints.
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