Vom Prompt zum Loop: Die Evolution der KI-Steuerung · Part 7 of 8
Verifizierung und Self-Correction
Testgetriebene KI-Entwicklung, Kritik-Schleifen und Fallbacks
A model must not grade its own work: generator-evaluator loops, hard tests and fallbacks for AI you can trust.
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About this audiobook
The most dangerous AI output is the confident wrong one. Part 7 is about verification: why a language model should never be the sole judge of its own work, and how the generator-evaluator principle and actor-critic setups build a second opinion into the system.
You learn to use objective feedback sources — compilers, linters, unit tests — as the hard ground truth, to build self-correction loops around them, and to design fallbacks and automated reviews for the cases that still slip through.
Published in German, 39 minutes. For anyone shipping AI-generated code or content into production.
Chapters
- Warum KI geprüft werden muss
- Generator, Evaluator und harte Tests
- Fehleranalyse und robuste Fallbacks
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See the courseVom Prompt zum Loop: Die Evolution der KI-Steuerung
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1. Das Handwerk der WorteRoles, few-shot examples, chain of thought and system instructions: prompting as a craft, not a guessing game.
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2. Kontext ist KönigHow to give a language model the right knowledge: context windows, memory and retrieval-augmented generation.
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3. Die Entstehung der AgentenFrom answering machine to actor: tool use, function calling, ReAct and the Model Context Protocol.
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4. Das Harness und die UmgebungThe environment decides whether an agent is production-ready: guardrails, sandboxes and automatic evaluators.
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5. Die Geburt von Loop EngineeringPlan, execute, observe, refine: why autonomous work cycles beat single prompts on every long task.
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6. Anatomie eines perfekten LoopsStop conditions, context rotation, memory and a human emergency stop: what keeps an autonomous loop safe.
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8. Das autonome ÖkosystemTeams of specialised agents, hierarchy versus swarm, and the human as architect, auditor and conductor.