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Observability for LLM Applications: Tracing, Evals, and Shipping AI You Can Trust
Paperback
Series: The AI Engineer's Library (Deutsche Ausgabe), Book 1
Publisher: Independently Published
Published: Jul 5 2026
Pages: 378
Weight: 1.11
Height: 0.78 Width: 6.00 Depth: 9.00
Language: German
Dein LLM-Feature ging letzten Donnerstag um 2 Uhr morgens live. Die Latenz ist in Ordnung. Die Fehlerrate liegt bei null. Und irgendwo, ganz leise, belügt es einen Kunden.
Klassische Observability sieht das nicht. CPU-Graphen, HTTP-Statuscodes und p99-Dashboards wurden für Systeme gebaut, die entweder funktionieren oder abstürzen. LLMs tun weder das eine noch das andere. Sie liefern einen selbstbewussten Satz, der Span schlie t grün, und der Fehler steckt im Inhalt.
Wenn du LLM-Features in Produktion auslieferst - als Backend-Engineer, als Plattform-Engineer, als SRE, der den Prompt von jemand anderem geerbt hat - dann ist dieses Buch das operative Handbuch, das dir bislang gefehlt hat. Es ist kein Theoriebuch über Transformer. Es ist keine Prompt-Engineering-Tour. Es ist der Stack, den du am Montagmorgen tatsächlich brauchst, um zu wissen, dass deine KI funktioniert.
Was du bekommst: die drei neuen Säulen (Traces, Evals, Kosten- und Drift-Metriken), zuerst auf Basis der anbieterneutralen OpenTelemetry-GenAI-Semantic-Conventions aufgebaut, dann ergänzt durch die Werkzeuge, die 2026 zählen - Langfuse, LangSmith, Arize Phoenix, Braintrust, DeepEval, Helicone und ein selbst gebauter Stack aus OTel Collector, ClickHouse und Grafana für Teams, die alles im eigenen Haus wollen. Jedes Werkzeug bekommt ein ehrliches Urteil: worin es am besten ist, worin es schwach ist, wann du es wählen solltest und was es kostet.
Du lernst, wie du einen kompletten LLM-Entscheidungspfad als Trace erfasst, Evals kontinuierlich in der CI und in Produktion ausführst, die Token-Kosten pro Nutzer und pro Feature verfolgst, Drift erkennst, bevor deine Nutzer es tun, und Incident-Response-Runbooks für einen Fehlermodus schreibst, den dein Pager noch nie gesehen hat. Echter Code in Python, Go und TypeScript. Echte Dashboards. Echte Traces.
Eine Ergänzung zu Hamel Husains Evals for AI Engineers (O'Reilly, 2026): Während jenes Buch tief in die Eval-Methodik für ML-Engineers eintaucht, deckt dieses den breiteren operativen Stack ab - Tracing, Tooling, Kosten, Drift und Bereitschaftsdienst - für den Plattform-Engineer als Leser.
Am Ende hast du eine Production-Readiness-Checkliste, die du gegen dein eigenes System laufen lassen kannst - und du meinst es ernst, wenn du deinem Chef sagst, dass die Antwort Ja lautet. Das erste Kapitel beginnt mit einem echten Incident. Am Montagmorgen hast du etwas zu tun.
Band 1 von The AI Engineer's Library.
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