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AI Models & Assistants / Knowledge library
AI Hallucination
Producing unsupported or false content with confident form
Why learn AI Hallucination?
Recognizing hallucination is essential when outputs inform decisions. Retrieval, tools, constrained outputs, and verification can reduce risk but do not eliminate it.
Where it came from
Generative models optimize predictive objectives rather than a built-in truth predicate. Hallucination describes fluent output not supported by sources or reality, including fabricated citations and details.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Identify hallucination: Which output is a hallucination?
- Require evidence: What is the strongest immediate control for answering from company policy documents?
- Design abstention: No retrieved passage answers the question. What should the system prefer?
- Evaluate factual support: Which evaluation best targets hallucination in a source-grounded task?
- Understand mitigation limits: A RAG response cites a retrieved page. Why can it still be wrong?
Documentation and sources
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Real World Example
Automated Regulatory Compliance Summarization
Legal and compliance departments in international financial institutions.
The business problem
Large language models generate confident but factually incorrect summaries of complex regulatory updates, leading to potential non-compliance and legal risk.
How this topic solves it
Implementing a Retrieval-Augmented Generation (RAG) architecture that constrains the model to ground its output exclusively in verified, ingested regulatory documents, effectively mitigating the generation of unsupported content.
- Ingest raw regulatory PDF documents into a vector database for semantic indexing.
- Implement a retrieval mechanism that fetches only relevant document chunks based on user queries.
- Inject the retrieved chunks into the model prompt as a mandatory context window.
- Apply a post-generation verification step to cross-reference claims against the source chunks.
- Configure the model to output a null response if the provided context does not contain the answer.
The flow from raw data to a grounded, verified response.
Indexed chunks
Query processor
Generation engine
Grounded output
- Regulatory Docs → ingest → Vector DB
- Retriever → search → Vector DB
- Vector DB → provide context → LLM
- LLM → generate → Final Summary
Expected business outcome
Increased reliability in automated compliance reporting by ensuring every generated claim is directly traceable to a specific regulatory source.
Limitations and checks
The system may suffer from reduced creative flexibility; check this by measuring the frequency of 'I cannot answer' responses when source data is sparse.