Konzepte, die du fühlen kannst.
Spar dir die 40-seitige Doku. Jede Erklärung verwandelt ein kniffliges KI-, Claude-Code-, MCP- oder Cloud-Konzept in ein animiertes, scrubbares Diagramm, das du ziehen und brechen kannst — bis die Idee in Minuten sitzt, nicht in Stunden.
Drei Schritte. Die Idee bleibt.
Die 60-Sekunden-Analogie lesen
Jedes Konzept beginnt mit einer kurzen Geschichte in Klartext. Kein Fachjargon — nur das mentale Modell, das du brauchst.
Die Live-Animation scrubben
Drücke Play, ziehe die Timeline oder benutze die Pfeiltasten. Sieh jeden Schritt Bild für Bild, bis der Ablauf klick macht.
Die Slider an die Grenze treiben
Justiere jede Variable. Das Diagramm reagiert sofort — so spürst du die Trade-offs und merkst dir die Grenzen.
Beliebteste Erklärungen
AI Cost Optimization: Cutting LLM Bills 80%
Most LLM bills can be cut by 50–90% without quality loss. Caching, model routing, prompt diet, and output caps deliver the bulk of it.
AI Observability: Tracing Every Token in Production
Without traces, every LLM bug is a guess. Capture prompts, tool calls, tokens, costs, and latencies for every request — searchable, filterable, alertable.
LLMOps: MLOps for the LLM Era
LLMOps is the operational discipline of running LLM apps in production — prompts as code, evals on every change, observability, cost, and incident response.
Wähle dein nächstes Konzept
Chain-of-Thought Prompting: Get LLMs to Show Their Work
Add "think step by step" and accuracy on multi-step problems jumps. Hide the scratchpad in production. Free quality, almost always.
ReAct Pattern: Reasoning + Acting in AI Agents
ReAct interleaves a Thought, an Action, and an Observation at each step. The "talk to yourself, then do, then look" loop powers most modern agents.
Tree of Thoughts: When LLMs Need to Branch and Backtrack
Tree of Thoughts explores multiple reasoning branches, prunes bad ones, and backtracks. Use it when the right path is not the first one the model picks.
Self-Consistency: Voting Across Multiple LLM Samples
Run the same prompt N times at non-zero temperature, take the majority answer. A few extra calls, big accuracy gains on hard reasoning.
Prompt Chaining: Breaking Complex Tasks Into Steps
Instead of one mega-prompt, chain N small prompts where each step's output feeds the next. Easier to debug, easier to evaluate, easier to evolve.
Reflexion and Self-Critique: AI That Reviews Its Own Work
Reflexion adds a critique-and-revise loop. The model produces output, criticises it, revises. A few cents extra; meaningful quality gain on the right tasks.
LLMOps: MLOps for the LLM Era
LLMOps is the operational discipline of running LLM apps in production — prompts as code, evals on every change, observability, cost, and incident response.
AI Observability: Tracing Every Token in Production
Without traces, every LLM bug is a guess. Capture prompts, tool calls, tokens, costs, and latencies for every request — searchable, filterable, alertable.
AI Cost Optimization: Cutting LLM Bills 80%
Most LLM bills can be cut by 50–90% without quality loss. Caching, model routing, prompt diet, and output caps deliver the bulk of it.
AI Latency: P50, P99, and Why TTFT Matters Most
Users feel TTFT (time to first token), not total time. Optimise for it. P99 hides the customers who actually churn — track it like your job depends on it.
Semantic Caching: Cache LLM Responses That Mean the Same
A normal cache matches exact keys. A semantic cache matches *meanings* — return the cached answer when the new query is close enough by embedding similarity.
LLM Routing: Right Model for Right Task, With Fallbacks
A router classifies each call and sends it to the cheapest model that handles it. Add fallbacks for outages and you get cheaper *and* more reliable than a single-model setup.
Hör auf, davon zu lesen. Fang an zu scrubben.
Festgefahren bei einem KI-, Claude-Code- oder Cloud-Konzept? Sag mir, was nicht klickt — ich liefere eine kostenlose interaktive Erklärung mit Analogie, Animation und Slidern, meist innerhalb einer Woche.
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