LLM Observability: How to Monitor, Trace, and Debug AI Applications

A successful API call does not mean a successful AI interaction.

The server can return HTTP 200 while the answer is wrong. The model can respond quickly while using stale retrieval evidence. An agent can produce the correct final sentence after choosing the wrong tool, retrying unnecessarily, or violating an approval boundary. A router can save money on average while quietly sending difficult requests to a model that fails more often.

Traditional application monitoring can tell you that a request happened. LLM observability should help you explain what happened inside the AI workflow and why the outcome changed.

LLM observability connects prompts, context, model calls, retrieval, tools, runtime settings, latency, cost, quality, and user outcomes into a traceable production record.

This guide explains how to design that visibility layer without logging everything indiscriminately or confusing observability with evaluation.

Quick Answer

A useful LLM observability architecture captures one end-to-end trace for each important request:

USER REQUEST
      ↓
PROMPT VERSION + RUNTIME CONTEXT
      ↓
ROUTING DECISION
      ↓
MODEL CALL
      ↓
RETRIEVAL / TOOLS / AGENT STEPS
      ↓
OUTPUT
      ↓
USER OR APPLICATION OUTCOME

OBSERVE ACROSS THE TRACE:
- model / provider
- prompt version
- context and retrieval metadata
- tool calls and tool results
- token usage
- cache usage
- latency
- retries
- cost
- errors
- quality / eval scores
- safety or policy signals
- final outcome
- user feedback

Do not start by storing full prompts and responses for every request. Start with structured metadata, stable identifiers, trace/span relationships, and the metrics needed to operate the system. Capture sensitive content only when the debugging or evaluation value justifies the privacy and storage risk.

Key Takeaways

• LLM observability is broader than API monitoring because AI failures are often semantic rather than operational.
• A trace should connect the user request to model calls, retrieval, tools, agent steps, output, and final application outcome.
• Metrics show trends; traces explain individual executions; logs/events record discrete facts; evals measure quality against criteria.
• Monitoring answers “what changed?” Observability helps answer “why?”
• Evaluation asks whether behavior meets a defined quality bar; debugging investigates a specific failure.
• Record prompt and workflow versions so production regressions can be tied to exact changes.
• Record the actual response model where the provider exposes it, not only the requested model.
• Token counts, cache usage, retries, and fallback paths matter for real cost.
• For RAG, observe retrieval separately from generation.
• For agents, trace the full trajectory including tools, arguments, state changes, retries, approvals, and stop behavior.
• Quality metrics should be linked to traces so you can inspect the runs behind a dashboard change.
• Do not log raw prompt or response content by default if it may contain PII, secrets, customer data, or regulated information.
• Sampling and redaction policies should be explicit.
• OpenTelemetry's GenAI conventions are useful for portable telemetry, but the GenAI conventions remain under active development.
• PrompTessor can improve the prompt artifact you are observing, but it is not a production tracing or observability backend.

Table of Contents

• What Is LLM Observability?
• Observability vs. Monitoring, Evaluation, and Debugging
• The Four Signals of LLM Observability
• Anatomy of an LLM Trace
• 1. Observe Prompts, Context, and Versions
• 2. Observe Model and Runtime Behavior
• 3. Observe Tokens, Cache Usage, and Cost
• 4. Observe Latency by Span, Not Only by Request
• 5. Observe Retrieval and RAG Quality
• 6. Observe Tools and Agent Trajectories
• 7. Connect Quality Evaluations to Production Traces
• 8. Connect Model Output to User and Business Outcomes
• 9. Handle Prompt Content, PII, and Sensitive Telemetry Carefully
• 10. Sampling, Retention, and Cost Control
• 11. Build Alerts Around AI Failure Modes
• OpenTelemetry for GenAI Observability
• Current Observability Patterns in Major AI Platforms
• Practical LLM Observability Examples
• A Production LLM Observability Stack
• From Observability to Debugging and Regression Tests
• Common LLM Observability Mistakes
• Where PrompTessor Fits
• LLM Observability Checklist
• Related PrompTessor Guides and Tools
• Official Resources
• FAQ

What Is LLM Observability?

LLM observability is the ability to inspect and explain the behavior of an AI application across its full execution path.

That path can include much more than one model call:

REQUEST
→ prompt assembly
→ context selection
→ model router
→ model call
→ retrieval
→ model call
→ tool call
→ tool result
→ model call
→ validation
→ output
→ user action

The goal is not to collect the largest amount of telemetry. The goal is to preserve enough evidence to answer operational questions such as why latency spiked, which prompt version produced a failure, which model handled the request, which documents were retrieved, whether an agent used the correct tool, why a router escalated, or whether users accepted, retried, abandoned, or corrected the answer.

Observability vs. Monitoring, Evaluation, and Debugging

Practice	Main Question	Typical Output
Monitoring	What changed?	Dashboards, thresholds, alerts
Observability	Why did it change?	Traces, correlated telemetry, drill-down
Evaluation	Does behavior meet our criteria?	Scores, pass/fail, benchmark results
Debugging	What caused this specific failure?	Root-cause hypothesis and targeted fix

For example, monitoring can show that groundedness fell. Observability can show the drop began after prompt v18 only on one route and with one retrieval index. Evaluation can replay the affected cases. Debugging can then identify the exact failed layer.

PrompTessor's AI Prompt Evaluation guide focuses on reusable quality tests. The Prompt Debugging Guide focuses on finding the failed layer and testing a targeted fix. Observability provides the production evidence that often tells you which cases deserve evaluation and debugging.

The Four Signals of LLM Observability

1. Traces

A trace represents one end-to-end operation or workflow. Spans can represent prompt assembly, model calls, retrieval, tools, router decisions, validation, handoffs, or custom application steps.

2. Metrics

Metrics aggregate behavior over time: requests, p50/p95/p99 latency, token usage, cost, cache-hit rate, tool failures, fallback rate, quality score, groundedness, schema failures, or agent loop rate.

3. Logs and Events

Events capture discrete facts such as a prompt version being selected, a tool call, an approval request, a retry, a policy failure, or a fallback activation. Logs should carry trace IDs so you can move from aggregate symptoms to exact executions.

4. Evaluations

Evaluation turns AI behavior into quality signals: correctness, groundedness, relevance, citation precision, tool-selection accuracy, policy compliance, or task completion.

Anatomy of an LLM Trace

TRACE: support_answer / request_98312

├─ request
│  ├ tenant
│  └ workflow_version
├─ prompt_assembly
│  ├ prompt_version = support-v18
│  └ context_sources = 4
├─ route_model
│  ├ route = standard
│  └ selected_model = model-B
├─ retrieve_policy
│  ├ source_ids
│  ├ document_dates
│  └ retrieval_scores
├─ model_call
│  ├ provider
│  ├ requested_model
│  ├ response_model
│  ├ input_tokens
│  ├ output_tokens
│  ├ cache_read_tokens
│  └ duration
├─ validation
│  ├ schema_pass = true
│  └ groundedness = 0.71
└─ outcome
   ├ user_feedback = negative
   ├ retry = true
   └ resolution = false

Not every application needs every field. Choose fields based on what can actually fail.

1. Observe Prompts, Context, and Versions

If a prompt changes in production, your observability data should make that change visible. Record stable identifiers such as prompt name, prompt version, workflow version, template variables, system/developer instruction version, and any model-specific prompt adapter version.

Do not rely only on storing the final rendered prompt. A version ID lets you aggregate success, latency, token, and cost changes by release.

PrompTessor's Prompt Versioning and Lifecycle Management guide covers the release side: version IDs, evaluations, regressions, rollout, and rollback.

Observe dynamic context separately with source IDs, retrieval timestamp, document version, memory IDs, context size, and truncation/compression behavior. This helps distinguish prompt failure from context failure.

2. Observe Model and Runtime Behavior

Record provider, requested model, response model where exposed, reasoning/thinking configuration where applicable, output mode, streaming state, finish reason, and retry/fallback behavior.

If you use an LLM router, also record router version, route, reason, candidate set, escalation, and fallback so quality changes can be separated from traffic-mix changes.

3. Observe Tokens, Cache Usage, and Cost

Track input tokens, output tokens, reasoning tokens where exposed, cache-read tokens, cache-write tokens where relevant, and total request cost.

A sudden increase in input tokens can indicate prompt growth, history bloat, excessive retrieval, duplicate context, or cache misses. A rise in output tokens can indicate instruction drift, formatting regressions, or agent loops.

PrompTessor's Prompt Caching guide explains why cache behavior should be measured separately from raw token volume.

Measure Cost per Successful Outcome

Average request cost can be misleading. A cheaper model with low task success and many retries can cost more per resolved task than a more capable first attempt.

4. Observe Latency by Span, Not Only by Request

An end-to-end latency number tells you the user waited six seconds. It does not tell you where the six seconds went.

TOTAL 6.4s
router            0.2s
retrieval         0.8s
model call #1     1.6s
tool call         2.7s
model call #2     0.9s
validation        0.2s

OpenAI's current Agents SDK tracing records model generations, tool calls, handoffs, guardrails, and custom events, illustrating this span-based approach for agent workflows.

Google's current AI agent observability guidance similarly highlights tracing for failed API requests, infinite execution loops, latency bottlenecks, agent communication, quality, and resource usage.

5. Observe Retrieval and RAG Quality

RAG should not appear as one opaque model request. Capture retrieval as its own operation, including safe query metadata, data source/version, retrieved document IDs, scores, document timestamps, chunk count, reranker output, and final chunks sent to the model.

This allows you to distinguish a model that faithfully answered from bad context from a model that ignored good context.

See RAG Prompting for the complementary prompt layer: authority, conflicts, citations, and missing evidence.

6. Observe Tools and Agent Trajectories

For agents, trace tool name, call ID, arguments or safe argument metadata, result status, duration, retries, approval state, state changes, handoffs, and completion reason.

Two runs can return the same final sentence while one uses correct tools and verification and the other loops, retries writes, or skips approval.

PrompTessor's AI Agent Evaluation guide explains how to evaluate these trajectories across task success, tools, arguments, state, recovery, authorization, stopping, latency, and cost.

Useful agent metrics include tool calls per task, model calls per task, retry rate, duplicate action rate, loop rate, handoff count, post-success actions, and cost per successful task.

7. Connect Quality Evaluations to Production Traces

Operational health is not enough. Depending on the use case, evaluate correctness, relevance, groundedness, citation precision, format validity, classification accuracy, tool-selection accuracy, policy compliance, or task completion.

Do not run every expensive grader on every production request by default. Common patterns include deterministic validators on every request, model-based graders on a sample, human review for high-risk cases, and offline replay on representative traces.

Quality scores become far more useful when attached to trace IDs because a dashboard drop can lead directly to the affected executions.

8. Connect Model Output to User and Business Outcomes

Where appropriate, attach downstream outcomes such as thumbs up/down, retry or regeneration, abandonment, support resolution, human escalation, task completion, conversion, or manual correction.

Use these as signals rather than automatic truth. A regeneration can mean dissatisfaction, but it can also mean exploration.

9. Handle Prompt Content, PII, and Sensitive Telemetry Carefully

Full prompt and response content can be extremely useful for debugging, but it can also contain names, account data, source code, credentials, health or financial information, or proprietary business data.

OpenTelemetry's 2026 GenAI observability guidance notes that full prompt, completion, tool-argument, and tool-result content can be captured when explicitly enabled, while metadata such as model names, token counts, and durations can be collected without enabling full content capture.

The current GenAI semantic-convention material also warns that input and output messages are likely to contain sensitive or personally identifiable information.

DEFAULT
metadata only

DEBUG SAMPLE
redacted content for selected traces

HIGH-RISK TENANT
no content capture

LOCAL / TEST
full content only under controlled conditions

Potential controls include PII masking, secret detection, field allowlists, content hashing, truncation, tenant-specific retention, and access control around raw traces.

10. Sampling, Retention, and Cost Control

Tracing every field of every request forever is rarely necessary. Use sampling deliberately.

Head Sampling

Decide whether to trace before completion. Useful for volume control, tenant tiers, or workflow-based coverage.

Tail Sampling

Keep traces after observing the result. Useful for retaining errors, high-latency runs, quality failures, fallbacks, high-cost runs, or unusual agent loops.

Retention Tiers

AGGREGATE METRICS   long retention
TRACE METADATA      medium retention
REDACTED CONTENT    shorter retention
FULL DEBUG CONTENT  shortest, tightly controlled

11. Build Alerts Around AI Failure Modes

Traditional infrastructure alerts still matter, but AI systems need additional signals.

Quality Alerts

Groundedness drops, task-success drops, citation failures, schema-validation failures, or user correction rate.

Cost Alerts

Tokens per request, cache-hit rate, cost per successful task, or router escalation rate.

Agent Alerts

Tool-call count, duplicate writes, unbounded retries, post-completion actions, or unauthorized-action attempts.

Routing Alerts

Fallback rate, strong-model route share, route quality, or router-vs-baseline performance.

Alerts should point to trace groups that explain the change.

OpenTelemetry for GenAI Observability

OpenTelemetry is increasingly useful as a portability layer for AI telemetry. Its semantic conventions define common names and meanings for telemetry across traces, metrics, logs, and resources.

The GenAI work covers concepts such as operation names, model/provider metadata, token usage, tool execution, agent/workflow operations, retrieval, and opt-in message/content capture.

The OpenTelemetry GenAI observability walkthrough demonstrates traces with model-call and tool-execution spans plus metrics such as operation duration and token usage.

Important caveat: the GenAI semantic conventions are under active development. OpenTelemetry's current documentation has moved GenAI conventions into a dedicated repository, and related conventions still include development-stage fields.

Use conventions where they improve interoperability, but document or pin the convention version, avoid treating experimental names as immutable contracts, and keep your internal telemetry model adaptable.

Current Observability Patterns in Major AI Platforms

OpenAI Agents SDK

OpenAI's Agents SDK tracing records end-to-end traces and spans for model generations, tool calls, handoffs, guardrails, and custom events, and positions tracing for debugging, visualization, and production monitoring.

Google Cloud Agent Observability

Google's AI agent observability guidance uses OpenTelemetry to observe agent decisions, tools, responses, latency bottlenecks, topology, quality, and resource usage. Google also provides Agent Platform tracing to inspect operation timelines and LLM/tool interactions.

Anthropic / Claude Code

Anthropic's current Claude Code monitoring documentation supports OpenTelemetry export for metrics and events and describes distributed trace backends for span correlation, with telemetry covering usage, cost, and tool activity.

LangSmith

LangSmith Observability treats traces as production records of what an LLM app or agent did and connects them to debugging, quality monitoring, and evaluation datasets. It also supports OpenTelemetry-based tracing.

Practical LLM Observability Examples

Example 1: Support Hallucination Rate Suddenly Increases

Groundedness drops from 94% to 82%. Trace segmentation shows the problem appears only on a new retrieval index while prompt and model versions remain stable. The root cause is retrieval freshness, not prompt wording.

Example 2: Cost Spikes After a Prompt Update

Cost per successful case rises 48%. Trace comparison shows input tokens doubled and cache-hit rate collapsed because a dynamic block was moved before a reusable prefix. The fix is prompt structure, not model pricing.

Example 3: Router Sends Too Much Traffic to the Expensive Model

Strong-model route share increases from 22% to 61%. Traces show router v7 classifies short extraction tasks as high-complexity when attachments exist. Update routing features and replay representative traffic.

Example 4: Agent Keeps Looping on Search

Tool calls per task rise from 4.1 to 12.7. Traces reveal repeated search/read cycles with no completion rule. Add stop criteria, then monitor loop rate and task success.

Example 5: Tool Call “Succeeds” but State Does Not Change

The update API returns success, but no verification span exists. The assistant tells the user the action completed. Add a verification read and false-success metric.

Example 6: JSON Is Valid but Semantically Wrong

Schema validation remains 99.9%, while downstream rejection rises. The syntax layer is healthy; classification semantics degraded. Add a semantic quality eval rather than tightening the schema.

Example 7: Model Migration Increases Latency

p95 latency increases. Span breakdown shows only a modest provider latency change, while reasoning configuration adds significant time and output tokens rise. Tune runtime and prompt policy before blaming the model alone.

Example 8: Prompt Version Improves Conversion but Lowers Groundedness

Conversion rises while groundedness and complaint rate worsen. Treat groundedness as a guardrail metric rather than allowing one business metric to erase a quality requirement.

A Production LLM Observability Stack

APPLICATION
   ↓
INSTRUMENTATION
OpenTelemetry / SDK hooks / custom spans
   ↓
COLLECTOR
OTLP / trace pipeline
   ↓
OBSERVABILITY BACKEND
traces + metrics + logs
   ↓
QUALITY PIPELINE
deterministic validators
sampled model graders
human review
   ↓
DASHBOARDS / ALERTS
   ↓
DEBUGGING + EVAL DATASETS

Instrument important operations, correlate them with shared IDs, store signals according to sensitivity, attach quality evaluation, and feed failures back into testing.

From Observability to Debugging and Regression Tests

PRODUCTION TRACE
      ↓
QUALITY / COST / LATENCY FAILURE
      ↓
TRACE GROUPING
      ↓
ROOT-CAUSE HYPOTHESIS
      ↓
TARGETED FIX
      ↓
OFFLINE REPLAY / EVAL
      ↓
REGRESSION CASE
      ↓
NEW VERSION
      ↓
PRODUCTION MONITORING

If the root cause is prompt-level, update the prompt. If it is retrieval, router policy, schema, permissions, model runtime, or tool behavior, fix that layer instead. Then convert the historical failure into a regression test.

Common LLM Observability Mistakes

1. Monitoring Only API Errors

Semantic failures often return successful HTTP responses.

2. Storing Only the Final Answer

You lose context, route, retrieval, tool, and runtime evidence.

3. Logging Full Prompts by Default

Raw content can contain sensitive data and create privacy risk.

4. No Prompt Version Field

You cannot correlate behavior with prompt releases.

5. No Model or Router Version

Traffic changes become indistinguishable from prompt changes.

6. One Giant Latency Metric

Without spans, bottlenecks remain hidden.

7. Measuring Cost but Not Task Success

Cheap failed requests are not necessarily cheap workflows.

8. Measuring Quality but Not Cost or Latency

A quality win can still make the product operationally worse.

9. Treating RAG as a Black Box

Observe retrieval inputs, outputs, source versions, and final context separately.

10. Evaluating Only Final Agent Text

Tool misuse, loops, duplicate writes, and authorization failures can hide behind a plausible answer.

11. No Trace-to-Eval Link

A quality dashboard is less useful if you cannot inspect the exact failing runs.

12. Alerts Without Debug Context

Alerts should link to affected traces, versions, models, and routes.

13. No Sampling Strategy

Full telemetry can become expensive, noisy, and risky.

14. Treating Experimental Telemetry Fields as Permanent

GenAI semantic conventions are evolving; version and abstract your instrumentation.

Where PrompTessor Fits

PrompTessor fits on the prompt-design and iteration side of an observability loop. Production telemetry can reveal that a prompt version has lower groundedness, one instruction causes longer outputs, or one target model follows a prompt less reliably.

When the root cause is prompt-level, PrompTessor can help analyze prompt quality, identify clarity/context/constraint weaknesses, generate optimized versions, refine prompts from observed feedback, and preserve reusable prompt workflows.

The public AI Prompt Analyzer can help inspect a prompt artifact, while the AI Prompt Optimizer can help create a clearer candidate after the failure has been diagnosed as prompt-level.

PRODUCTION OBSERVABILITY
        ↓
FAILURE CLUSTER
        ↓
ROOT CAUSE
        ↓
PROMPT-LEVEL?
   ├ YES → PrompTessor Analyze / Optimize / Refine
   │       → Eval / replay → Release
   └ NO  → Fix retrieval / tools / router /
           runtime / schema / application state

PrompTessor does not provide production trace ingestion, telemetry storage, OpenTelemetry collectors, live latency monitoring, cost attribution, alerting, agent trajectory dashboards, or end-to-end observability infrastructure.

Use observability to discover what changed and why. Use PrompTessor when the evidence points to the prompt itself.

LLM Observability Checklist

• Can every important request be tied to a trace ID?
• Can spans show where time was spent?
• Do you record prompt name and version?
• Do you record workflow/deployment version?
• Do you record requested and actual model where available?
• Do you record router version and route decision?
• Can you identify fallback and escalation events?
• Do you record input/output tokens?
• Do you record cache usage where available?
• Can you estimate total request cost?
• Do you measure cost per successful outcome?
• Can you separate retrieval latency from generation latency?
• Do RAG traces include source IDs and versions?
• Can you detect stale retrieval sources?
• Do tool traces include tool name, status, duration, and retries?
• Can you identify duplicate or post-success agent actions?
• Do you observe approval/authorization events?
• Are deterministic validators attached to traces?
• Are sampled semantic quality evals attached to traces?
• Can dashboard changes be drilled down to failing traces?
• Are product outcomes correlated where appropriate?
• Do you have a policy for raw prompt/response content?
• Is sensitive content redacted or excluded?
• Do you use head or tail sampling intentionally?
• Do retention periods differ by sensitivity?
• Are quality alerts separate from infrastructure alerts?
• Do cost alerts include retry/escalation behavior?
• Do latency alerts link to span breakdowns?
• Can historical failures become evaluation cases?
• Can you compare prompt/model/router versions under equivalent workloads?
• Is your telemetry schema versioned?
• Are experimental GenAI conventions isolated behind an instrumentation layer?

Related PrompTessor Guides and Tools

• AI Prompt Evaluation
• Prompt Debugging Guide
• LLM Routing Guide
• AI Agent Evaluation
• Prompt Versioning and Lifecycle Management
• RAG Prompting
• Function Calling and Tool Use
• Prompt Caching
• AI Prompt Analyzer
• AI Prompt Optimizer

Official Resources

• OpenTelemetry — Inside the LLM Call: GenAI Observability
• OpenTelemetry — Semantic Conventions
• OpenAI Agents SDK — Tracing
• Google Cloud — Observability for AI Agent Developers
• Google Cloud — Agent Platform Tracing
• Anthropic — Claude Code Monitoring With OpenTelemetry
• LangSmith — Observability
• LangSmith — Trace With OpenTelemetry

FAQ

What is LLM observability?

LLM observability is the ability to inspect and explain an AI application's behavior across prompts, context, models, retrieval, tools, runtime settings, latency, cost, quality, and final outcomes.

How is LLM observability different from monitoring?

Monitoring usually detects changes through dashboards and alerts. Observability adds enough correlated telemetry to investigate why the change happened.

How is LLM observability different from evaluation?

Observability records and connects production behavior. Evaluation applies explicit criteria to measure whether outputs or trajectories are good enough.

What is an LLM trace?

An LLM trace is an end-to-end record of one AI request or workflow, usually composed of spans for prompt assembly, routing, retrieval, model calls, tools, validation, and outcome.

What should I log for every LLM request?

Consider trace ID, workflow and prompt versions, model/provider, latency, token usage, error status, and relevant route/context metadata. Content capture depends on privacy requirements.

Should I log full prompts and responses?

Not by default. Full content may contain PII, secrets, customer data, or proprietary content, so use explicit capture, redaction, access controls, sampling, and retention policies.

What metrics matter for LLM observability?

Common metrics include latency, request volume, errors, tokens, cache usage, cost, task success, groundedness, schema validity, tool-call count, retry rate, fallback rate, and cost per successful outcome.

How do I observe RAG applications?

Trace retrieval separately from generation and record source/version, document IDs, retrieval scores, timestamps, final context, output, and grounding quality.

How do I observe AI agents?

Trace model calls, tools, arguments, results, state changes, handoffs, retries, approvals, verification, stop behavior, tokens, latency, cost, and final outcome.

What is OpenTelemetry's role in LLM observability?

OpenTelemetry provides provider-neutral telemetry standards and transport patterns, including evolving GenAI semantic conventions for model operations, token usage, tools, agents, and retrieval.

What is tail sampling?

Tail sampling decides whether to retain a trace after observing its outcome and is useful for keeping errors, slow runs, quality failures, fallback events, or expensive requests.

How do I monitor LLM quality in production?

Combine deterministic validators with sampled semantic evaluations, user signals, and offline replay, and attach quality scores to trace IDs.

Can observability help prompt debugging?

Yes. Production traces can reveal whether a failure correlates with a prompt version, model route, retrieval source, tool result, runtime setting, or upstream workflow change.

Does PrompTessor provide LLM observability?

No. PrompTessor helps generate, analyze, optimize, refine, and organize prompt artifacts. Production trace ingestion, OpenTelemetry infrastructure, monitoring, cost attribution, and observability dashboards remain outside PrompTessor.

Conclusion

LLM observability is not about collecting every possible field. It is about preserving enough structured evidence to explain production behavior.

REQUEST
   ↓
PROMPT + CONTEXT
   ↓
ROUTER
   ↓
MODEL / RETRIEVAL / TOOLS
   ↓
OUTPUT
   ↓
OUTCOME

observed through
TRACES + METRICS + LOGS / EVENTS + EVALUATIONS

Start with stable identifiers and trace structure. Record prompt, workflow, model, and router versions. Separate retrieval and tool calls from model spans. Measure token usage, cache usage, cost, and latency by component. Attach quality evaluations to trace IDs. Keep sensitive content capture opt-in and controlled. Use sampling and retention intentionally. Turn production failures into debugging cases and regression tests.

The goal of LLM observability is not to know that your AI application produced an answer. It is to know what happened, why it happened, and what changed when behavior became better or worse.

Improve the Prompt When the Trace Points to the Prompt

When production evidence shows that the prompt artifact is the failed layer, PrompTessor can help analyze the prompt, identify weaknesses, create optimized versions, refine from observed feedback, and preserve reusable prompt iterations for retesting.