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HTTP 200 only proves that a request completed at the transport layer. It does not show whether the model used the intended prompt, whether retrieval supplied relevant evidence, whether a tool call failed silently, whether an agent took an unsafe path, or whether the answer was correct. Reliable LLM monitoring combines structured logs, metrics, distributed traces, continuous evaluations, and governance for sensitive telemetry.
This guide shows what to capture, how to trace a request through retrieval and agent steps, how to measure latency, tokens, cost, quality and safety, and how to turn those signals into actionable alerts.
Why are HTTP status codes insufficient for an LLM application?
HTTP status describes the endpoint interaction, not the probabilistic work performed inside it. A successful response can contain an ungrounded RAG answer, an incorrect calculation, a policy violation, an unnecessary tool call, or a response generated from an outdated prompt. Conversely, a transient provider timeout may be retried internally and never appear as an application-level error.
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- Transport health: HTTP status, connection errors and endpoint availability.
- Execution health: model calls, retrieval, guardrails, tools, retries, fallbacks and timeouts.
- Economic health: token volume, request volume and estimated cost.
- Outcome health: correctness, groundedness, relevance, safety, tool-use correctness and goal completion.
A production dashboard should expose all four layers. A green transport panel must not hide a failing quality or cost panel.
What should I log for every LLM request?
Create a structured record for each user interaction and each significant operation inside it. Use stable identifiers so a support engineer can move from a user report to the exact trace, model call, retrieved documents and tool result.
Identity and release context
- Request, user, tenant, conversation and run IDs.
- Start and end timestamps, route or feature name, deployment region and release or feature version.
- Prompt-template version, retriever version, tool or workflow version and any experiment cohort.
Model invocation metadata
- Provider, model name and model version.
- Temperature, top-p, maximum output tokens and other generation settings.
- Input and output token counts, total tokens, finish reason and cache-hit status.
- Fallback model, if a primary model was unavailable or exceeded a limit.
Timing and failure details
- End-to-end duration, time to first token and inter-token delay for streaming responses.
- Duration for every child operation, including retrieval, prompt construction, model calls, guardrails and tools.
- Timeouts, retries, throttling, provider error codes and the final disposition returned to the user.
Cost and volume dimensions
- Request count and token totals by application, route, user, role, tenant, model and feature.
- Estimated cost using the applicable provider rate and model version.
- Whether cached input, batching or a fallback changed the cost calculation.
OpenTelemetry guidance specifically recommends model request and response metadata, token and cost counters, request volume and duration. Keep these as structured fields and metrics rather than parsing text from application logs later.
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How do I trace one LLM interaction from request to answer?
Represent one user interaction as a distributed trace. Create child spans for each meaningful step, propagate the same correlation IDs across services, and attach evaluation results to the span that produced the output.
- Start a root trace. Assign a run ID when the application accepts the request. Include tenant, user and release context subject to your privacy policy.
- Record input handling. Create spans for authentication, conversation lookup, prompt-template selection and input moderation.
- Trace retrieval. Capture query rewriting, retriever name and version, search duration, document IDs, ranking scores, selected chunks and context size.
- Trace every model call. Record model settings, token counts, finish reason, cache status, latency and provider errors.
- Trace guardrails and post-processing. Include safety classifiers, schema validation, citation checks, formatting and response filtering.
- Trace tools and agent decisions. Give every invocation its own span with arguments, permissions, result, error, execution time, retry count and approval state.
- Close the trace with the user outcome. Record the final status, streamed-token completion, refusal category or fallback path.
Large prompts, responses and retrieved passages may exceed practical span-attribute limits. Store them as protected events or linked payloads, and keep compact hashes, sizes and classifications in the span itself. OpenTelemetry’s LLM guidance warns that backends can struggle with very large attributes.
trace: run_id, user_id, release_version
span: retrieval
query_version, document_ids, scores, latency_ms
span: model_call
provider, model, prompt_version, input_tokens, output_tokens
span: tool_call
tool, permission, arguments_redacted, result, latency_ms
span: evaluation
groundedness, correctness, safety, goal_completionUse OpenTelemetry GenAI semantic conventions where supported. A vendor-neutral schema lets traces move between compatible backends and keeps instrumentation separate from a particular observability product.
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How can I tell whether a RAG answer was grounded?
Groundedness requires connecting the final answer to the evidence retrieved for that request. Logging only the user query and answer cannot show whether the model saw the right document or ignored it.
Capture the retrieval path
- Original query and any rewritten or decomposed queries.
- Retriever and index name, version and configuration.
- Document and chunk identifiers, ranking scores and retrieval latency.
- Number of chunks, context-window size and truncation events.
- Whether retrieval returned no results, low-confidence results or a fallback corpus.
Evaluate evidence support
For sampled traces, score whether each material claim is supported by the retrieved passages. Keep separate measures for retrieval relevance and answer groundedness: a relevant document can still be misread, and a fluent answer can contain claims absent from every retrieved chunk. Store evaluator output on the retrieval or generation span so a quality regression can be compared with the exact retriever, prompt and model versions.
Alert on retrieval-specific failures
Track retrieval-empty events, sudden score-distribution changes, context truncation and drops in groundedness by index version, tenant and release. Link alerts to representative traces containing the query, selected chunks and final answer, with sensitive text redacted according to policy.
How do I trace tool calls and autonomous agent decisions?
An agent needs an execution-path trace, not just a final completion log. Make the goal, plan, model step, tool call, observation, memory update, verification and replan visible as a sequence.
Tool-call fields
- Tool name and version, declared permissions and approval requirement.
- Arguments after redaction or hashing, validation result and authorization decision.
- Start and end times, result size, error type, retry count and timeout.
- Whether the model, policy engine or human approved the action.
Agent-path fields
- Initial goal and plan identifier.
- Step number, model decision and selected tool.
- Observation returned by the tool and any memory read or write.
- Checkpoint, recovery action, replan reason and human intervention.
- Budget consumed in tokens, time, tool calls or monetary cost.
Hugging Face identifies retries, recovery, memory accesses, checkpoints, human interventions, tool failures and budget consumption as useful long-horizon signals. These fields make it possible to distinguish a slow model from an agent that repeatedly replans or waits on a failing dependency.
Which metrics belong on an LLM monitoring dashboard?
Use counters, rates, distributions and segmented views. Average values alone hide tail behavior and cohort-specific failures.
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| Area | Metrics to display | Useful breakdowns |
|---|---|---|
| Traffic | Requests, completed runs, streaming completions and cancellations | Route, tenant, geography, model and release |
| Reliability | Errors, timeouts, throttling, retries, fallback rate and empty retrievals | Provider, tool, retriever and failure category |
| Latency | End-to-end, time to first token, inter-token delay, model-step and tool latency; average, P90 and P99 | Model, route, region, cohort and release |
| Usage and cost | Input, output and total tokens; estimated cost; cache hits | Application, user role, feature, model and tenant |
| Quality | Correctness, relevance, groundedness, factuality, tool-use correctness and goal completion | Prompt, model, retriever, tool version and evaluation set |
| Safety | Toxicity, policy violations, unsafe-content detections and refusal quality | Policy category, route, model and release |
AWS CloudWatch’s generative-AI guidance lists invocation totals, token measures, average/P90/P99 latency, errors, throttling and cost attribution as dashboard measures. Apply the same percentile approach to internal spans so a fast average does not conceal a painful tail.
What should trigger an alert?
Alert on behavioral deviations and user-impacting thresholds, not on every unusual trace. Establish a baseline for each important route and cohort, then alert when the current distribution departs from it.
- Sudden increases in input or output tokens, estimated cost or context size.
- P95 or P99 end-to-end, model or tool latency regressions.
- Rising provider errors, timeouts, throttling, retries or fallback storms.
- Declining tool-success rate or repeated failures from one tool version.
- Quality-score drops for correctness, groundedness, safety or goal completion.
- Unsafe-content detections or policy violations above the approved rate.
- Retrieval-empty events, score-distribution shifts or frequent context truncation.
Every alert should link to representative traces and show the affected model, prompt, retriever, tool, tenant and release. A page that says “quality down” without examples forces responders to reproduce a probabilistic failure manually.
How do I continuously evaluate answer quality and safety?
Combine offline regression tests with sampled production evaluation. Neither replaces the other: a fixed test set catches known regressions, while real traffic reveals new queries, tools and failure modes.
- Build a versioned evaluation set. Include normal, edge, adversarial and high-value tasks. Store expected behavior or scoring criteria, not only a preferred answer.
- Run pre-release evaluations. Test prompt, model, retriever and tool changes before deployment. Compare scores and fail a release gate when a critical metric regresses beyond the team’s approved tolerance.
- Sample production traces. Score correctness, groundedness, relevance, factuality, safety, tool-use correctness and user goal completion.
- Attach results to traces. Record evaluator version, rubric, score, confidence and reviewer identity on the generating span.
- Segment comparisons. Compare by model version, prompt version, retrieval configuration, tool version, route and user cohort.
- Close the loop. Confirm failures, add representative cases to the evaluation set and rerun them at the next release.
Middleware describes this cycle as instrument, trace, evaluate, monitor and improve. MLflow documents trace-linked feedback, production monitoring and building datasets from real-world traffic. Human review remains important for ambiguous or high-risk cases; automated judges should be treated as measurement instruments with their own validation.
Which implementation approach should I choose?
Choose based on portability, control, data residency and the depth of evaluation you need. A managed service can reduce operational work, while a self-hosted stack can provide tighter control over sensitive traces.
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| Approach | Strengths | Questions to verify |
|---|---|---|
| OpenTelemetry-first | Vendor-neutral traces and GenAI semantic conventions; easier backend changes | Does the backend preserve model, retrieval and tool fields, and handle payload links? |
| Open-source or self-hosted | Control over storage, retention, redaction and network location; trace-linked evaluation | Who operates collectors, storage, upgrades, access control and alerting? |
| Hosted observability | Ready-made dashboards, traces, evaluations and metrics | Are residency, retention, framework coverage, redaction and export adequate? |
| Cloud-native services | Integration with provider metrics, identity and deployment tooling | Which features vary by region, model, account type or deployment mode? |
Compare products on trace completeness, OpenTelemetry interoperability, prompt and response redaction, evaluator support, percentile and cost metrics, retention and residency, alerting, framework coverage, self-hosting requirements and total operating cost. Avoid selecting solely on the appearance of a dashboard; verify that a single trace can connect retrieval, generation, tools and evaluation.
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LLM telemetry often contains personal, confidential or regulated information. Define a telemetry data contract before enabling full-content capture.
- List every field collected and its purpose.
- Redact, tokenize or hash personal and secret values before export.
- Set retention periods by data class and document deletion handling.
- Restrict raw-content access by role and log access to traces.
- Encrypt telemetry in transit and at rest.
- Specify storage location and residency requirements.
- Sample, summarize or retain hashes when full payloads are unnecessary.
- Keep tool arguments and retrieved documents under the same controls as user prompts.
Microsoft’s observability guidance emphasizes privacy, data minimization, residency, retention, access controls and encryption aligned with policy and risk. MLflow documents PII redaction and tracing controls. Redaction must occur before data leaves the application boundary; masking only in the dashboard can leave sensitive values in collectors or backups.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I troubleshoot common LLM monitoring failures?
The dashboard shows 200 responses, but users report wrong answers
Open a trace and inspect prompt version, model version, retrieval documents, scores, context truncation and groundedness evaluation. Compare affected traces with the last known-good release, then add the confirmed failure to the regression set.
Latency rose without an increase in HTTP errors
Break end-to-end time into time to first token, model spans, retrieval, guardrails and tools. Check P99 rather than only the average, then look for retries, throttling, a fallback model or an agent replan loop.
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Compare input and output tokens, context size, cache-hit rate, model mix and fallback usage by route and tenant. A prompt expansion or retrieval change can increase input tokens even when the number of requests is unchanged.
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An agent appears stuck
Inspect step count, repeated plans, tool retries, memory operations, checkpoint recovery and remaining budget. Set explicit limits for time, tokens and tool calls, and alert when a run approaches them.
Evaluation scores are hard to trust
Record evaluator version, rubric and confidence, compare automated scores with human-reviewed samples, and segment results by task type. Investigate score shifts that coincide with evaluator changes before attributing them to the application.
What is a practical rollout sequence?
Instrument in layers so each release produces useful evidence without exposing more content than necessary.
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- Add correlation IDs and root traces to every user interaction.
- Capture model metadata, token counts, latency, errors and cost dimensions.
- Add retrieval and tool child spans, including versions and durations.
- Publish dashboards with percentile latency, reliability, usage and cost.
- Create a small versioned evaluation set and run it before changes.
- Sample production traces for quality and safety, attaching scores to spans.
- Set behavioral alerts and link each alert to representative traces.
- Review traces regularly, promote confirmed failures into regression tests and remove telemetry fields that no longer serve a defined purpose.
This sequence produces an observability system that explains not only whether an LLM request completed, but what the system did, what it cost, how long it took, whether its evidence supported the answer and whether the outcome met your safety and product goals.
Frequently Asked Questions
What is the difference between LLM observability and application monitoring?
Application monitoring focuses on service health such as uptime, errors and resource saturation. LLM observability adds model, prompt, retrieval, tool, token, cost, quality and safety signals so teams can explain probabilistic behavior.
Should prompts and model responses be stored in every trace?
No. Store full content only when the debugging or evaluation value justifies the privacy risk. Otherwise retain redacted excerpts, hashes, metadata or short-lived protected payloads governed by a documented retention policy.
How do I monitor streaming responses?
Measure time to first token, inter-token delay, completion time and cancellation rate in addition to total latency. Keep the same run and span IDs across the stream so partial output, retries and disconnects remain correlated.
Can I compare two prompt or model versions using production traffic?
Yes. Record prompt and model versions on every trace, assign traffic to explicit cohorts, and compare segmented quality, latency, token and cost distributions. Do not combine cohorts when a release changes retrieval or tool behavior as well.
What is a fallback storm in an LLM system?
It is a period when many requests switch from the primary model or provider to a fallback, often after throttling, timeouts or errors. Monitor fallback rate and reason, because the backup may have different latency, limits, cost and answer quality.
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