Frequently Asked Questions
Frequently Asked Questions
Common questions about the workshop, what to expect, and how to prepare.
Why do LLM agents hallucinate more than single-call LLMs? +
LLM agents hallucinate more than single-call LLMs because each agent interaction adds to the context window, stale information from earlier turns persists and influences later outputs, and multiple agents can reinforce each other's hallucinations when they share context without independent verification. Context engineering prevents this through explicit context management and per-agent RAG grounding.
What is the most effective way to stop LLM agent hallucination? +
The most effective hallucination prevention is citation-grounded generation — requiring every factual claim in an agent's output to be explicitly attributed to a retrieved source. When the agent cannot cite a source, it must flag the claim as uncertain rather than generating it confidently. This RAG-with-citations approach, combined with semantic blueprint constraints that limit agents to their specific domain, dramatically reduces hallucination in production.
How does the Glass-Box architecture help prevent LLM agent hallucination? +
The Glass-Box architecture makes hallucination visible and catchable. Because every context passing step and agent output is logged and traceable, you can identify when an agent generated an uncited claim, which context led to it, and which downstream agents were affected. This observability allows you to detect hallucination patterns and improve your safeguards systematically rather than discovering failures in production.
Can safeguards completely eliminate LLM agent hallucination? +
No system completely eliminates LLM hallucination, but the architectural safeguards taught in this workshop reduce it to manageable levels for production use. Citation grounding, output validation, and domain-constrained semantic blueprints together catch the vast majority of hallucination events. The Glass-Box logging layer helps you identify and address the remaining edge cases over time.
What is context window overflow and how does it cause hallucination? +
Context window overflow occurs when the accumulated context for an agent exceeds its effective context window, causing the model to lose track of earlier information, contradict itself, or generate outputs disconnected from the actual task. The memory engineering techniques in this workshop — including context compression, selective retrieval, and explicit context lifecycle management — prevent context window overflow from causing hallucination.
How do I validate LLM agent outputs to catch hallucination before users see it? +
The workshop covers output validation patterns including: citation verification (checking that claims reference retrievable sources), factual consistency checking between agent outputs in the same workflow, domain constraint validation against semantic blueprint specifications, and confidence scoring that flags uncertain outputs for human review. These validation layers form a hallucination defense-in-depth architecture.
