What's changed: Added per-section figures (cert-figure-retrofit). New AI-300 Chapter 4 (Domain 4 "GenAI quality assurance and observability": evaluation = test datasets/data mapping/AI quality metrics (groundedness/relevance/coherence/fluency)/risk-safety evaluation/automated evaluation workflows; observability = Foundry continuous monitoring/performance (latency/throughput/response time)/cost (token consumption/resource usage)/logging-tracing-debugging)
4.2Observability for generative AI apps and agents
Understand continuous monitoring in Foundry, monitoring performance metrics (latency, throughput, response times), tracking and optimizing cost metrics (token consumption, resource usage), and configuring detailed logging, tracing, and debugging for production troubleshooting.
Generative AI apps are continuously monitored in production to understand performance, cost, and failures and to improve. This is observability, which supports the GenAIOps operations loop.
4.2.1Monitoring performance and cost
With continuous monitoring in Foundry, track performance metrics: latency (delay to respond), throughput (work per unit time), and response time. A key generative-AI metric is cost, especially token consumption (input + output tokens) and resource usage. Because token consumption maps directly to cost, reduce it via concise prompts, caching, and appropriate model selection.
4.2.2Logging, tracing, and debugging
Production troubleshooting needs detailed logging, tracing, and debugging. Tracing visualizes how a request flows through app → retrieval (RAG) → model → tools, pinpointing causes of latency or failure (distributed tracing). For multi-step flows like agents, tracing is especially valuable. With logs, you quickly find the root cause of quality degradation or errors.
| Aspect | Metric/means | Key point |
|---|---|---|
| Response speed | Latency / response time | Watch delay |
| Volume | Throughput | Work per unit time |
| Cost | Token consumption / resource usage | Tokens map to cost |
| Root cause | Logging / tracing / debugging | Visualize request flow |
Watch the mix-ups: (1) performance (latency/throughput) vs cost (token consumption) are separate axes—token consumption is key for generative AI. (2) logging (recording) vs tracing (visualizing request flow = root cause) differ. (3) Observability maintains quality and efficiency even "after deployment."
Map what to measure → means: "watch response delay" = latency; "watch volume" = throughput; "reduce cost" = monitor/optimize token consumption; "pinpoint latency/failure causes" = tracing (distributed tracing); "investigate production issues" = detailed logging + debugging.
4.2.3Section summary
- Continuous monitoring in Foundry: performance (latency/throughput/response time)
- Monitor/optimize cost via token consumption/resource usage (tokens map to cost)
- Use logging/tracing/debugging to visualize request flow and find root causes
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Quick check
(just a quick review)Q1. Which metric should be monitored and optimized because it maps directly to generative AI cost?
Q2. Which visualizes how a request flowed through app → retrieval → model → tools to pinpoint latency/failure causes?
Q3. Which performance metric represents the delay until a response?
Q4. Which operations concept continuously monitors a generative AI app in production for performance, cost, and failures?
Q5. Which is NOT an appropriate way to reduce cost by lowering token consumption?
Q6. Which correctly distinguishes logging from tracing?
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