Data Scientist → AI Engineer • Training 14

Article-Training • AI Engineering Foundations

LLMOps

Operating LLM Applications in Production

Bring prompts, models, retrieval, agents, evals and guardrails under disciplined versioning, deployment, monitoring, cost control and incident management.

Version → Test → Deploy → Observe → Evaluate → Improve → Roll Back

📦 VERSION

→

🧪 TEST

→

🚀 DEPLOY

📡 OBSERVE

→

💰 OPTIMIZE

→

↩️ ROLLBACK

8learning modules

24interactive practices

5rapid review questions

50%certificate threshold

Participant name

Learning target

Operate AI applications as versioned, observable systems with measurable quality, cost, latency and safety.

Practice progress0 / 24

Complete 12 of 24 practices (50%) and enter your name to unlock the Certificate of Participation.

MODULE 01

🏗️

What LLMOps Operates

LLMOps covers more than model deployment: prompts, retrieval configs, eval sets, tool schemas, guardrails and data/index versions all affect behavior.

👁️

See it this way

The production artifact is the whole AI system configuration.

Core ideas

Track model and prompt versions
Track retrieval and tool configuration
Track eval and guardrail versions

Production sketch

release = {'model':'v3','prompt':'p12','index':'i7','eval':'e5'}

✅

Operate the complete AI configuration, not only the foundation model name.

Practice — 3 cases

Practice 1 / Práctica 1

Which principle best matches What LLMOps Operates?

AVersion the full behavior-defining system configuration.BIgnore the module's control and rely on intuition only.CAssume production behavior never needs verification.

Best answer: A. Version the full behavior-defining system configuration.

Practice 2 / Práctica 2

Which behavior is the clearest anti-pattern for What LLMOps Operates?

AVersion and observe the relevant artifact.BTest representative cases before release.CRecord only the model name and ignore prompts, tools and indexes.

Best answer: C. Record only the model name and ignore prompts, tools and indexes.

Practice 3 / Práctica 3

What should a production team check for What LLMOps Operates?

AOnly whether one demo looks impressive.BCheck that every behavior-defining artifact has a version.COnly the UI theme.

Best answer: B. Check that every behavior-defining artifact has a version.

MODULE 02

📦

Versioning and Reproducibility

A production result should be reproducible enough to identify which model, prompt, retrieval and tool versions produced it.

👁️

See it this way

If you cannot name the configuration, you cannot compare releases reliably.

Core ideas

Use immutable release identifiers
Store config alongside deployment
Record source/index versions

Production sketch

release_id='aiapp_2026_09_23_01'
log(release_id, request_id)

✅

Reproducibility starts with explicit version identifiers.

Practice — 3 cases

Practice 4 / Práctica 4

Which principle best matches Versioning and Reproducibility?

AUse immutable release IDs and record configuration lineage.BIgnore the module's control and rely on intuition only.CAssume production behavior never needs verification.

Best answer: A. Use immutable release IDs and record configuration lineage.

Practice 5 / Práctica 5

Which behavior is the clearest anti-pattern for Versioning and Reproducibility?

AVersion and observe the relevant artifact.BTest representative cases before release.CEdit production prompts in place with no history.

Best answer: C. Edit production prompts in place with no history.

Practice 6 / Práctica 6

What should a production team check for Versioning and Reproducibility?

AOnly whether one demo looks impressive.BCheck release lineage and ability to identify prior configuration.COnly the UI theme.

Best answer: B. Check release lineage and ability to identify prior configuration.

MODULE 03

🚀

Environments and Deployment

Use controlled environments so changes can be tested before production and promoted consistently.

👁️

See it this way

A production prompt should not be the first place a change is tested.

Core ideas

Separate dev/test/prod
Promote versioned artifacts
Keep rollback-ready deployments

Production sketch

deploy(release, env='staging')
if gate_passes(): promote('prod')

✅

Promote tested releases through controlled environments.

Practice — 3 cases

Practice 7 / Práctica 7

Which principle best matches Environments and Deployment?

ATest and promote the same versioned artifact through environments.BIgnore the module's control and rely on intuition only.CAssume production behavior never needs verification.

Best answer: A. Test and promote the same versioned artifact through environments.

Practice 8 / Práctica 8

Which behavior is the clearest anti-pattern for Environments and Deployment?

AVersion and observe the relevant artifact.BTest representative cases before release.CChange production directly without a tested release artifact.

Best answer: C. Change production directly without a tested release artifact.

Practice 9 / Práctica 9

What should a production team check for Environments and Deployment?

AOnly whether one demo looks impressive.BCheck environment parity, gate status and rollback readiness.COnly the UI theme.

Best answer: B. Check environment parity, gate status and rollback readiness.

MODULE 04

📡

Observability for AI Systems

Observe latency, token usage, model errors, retrieval quality, tool calls, refusals, fallbacks and business outcomes.

👁️

See it this way

AI telemetry must connect technical behavior with task success.

Core ideas

Trace request lifecycle
Log versions and key metrics
Protect sensitive content in logs

Production sketch

trace(request_id, model, prompt_v, latency, tokens, outcome)

✅

Observability should explain both system performance and user-visible quality.

Practice — 3 cases

Practice 10 / Práctica 10

Which principle best matches Observability for AI Systems?

ATrace requests with version, latency, usage and outcome signals.BIgnore the module's control and rely on intuition only.CAssume production behavior never needs verification.

Best answer: A. Trace requests with version, latency, usage and outcome signals.

Practice 11 / Práctica 11

Which behavior is the clearest anti-pattern for Observability for AI Systems?

AVersion and observe the relevant artifact.BTest representative cases before release.CLog only successful final text and discard all operational context.

Best answer: C. Log only successful final text and discard all operational context.

Practice 12 / Práctica 12

What should a production team check for Observability for AI Systems?

AOnly whether one demo looks impressive.BCheck traces, error rates, latency, cost and outcome metrics.COnly the UI theme.

Best answer: B. Check traces, error rates, latency, cost and outcome metrics.

MODULE 05

💰

Cost and Latency Engineering

Model choice, context size, retrieval depth and agent steps directly affect user experience and operating cost.

👁️

See it this way

Quality gains must justify their latency and cost.

Core ideas

Measure tokens and requests
Use smaller models where sufficient
Cache stable work and bound loops

Production sketch

cost = tokens_in*rate_in + tokens_out*rate_out
assert latency_p95 < target

✅

Optimize cost and latency against a quality floor, not in isolation.

Practice — 3 cases

Practice 13 / Práctica 13

Which principle best matches Cost and Latency Engineering?

AMeasure quality, cost and latency together when optimizing.BIgnore the module's control and rely on intuition only.CAssume production behavior never needs verification.

Best answer: A. Measure quality, cost and latency together when optimizing.

Practice 14 / Práctica 14

Which behavior is the clearest anti-pattern for Cost and Latency Engineering?

AVersion and observe the relevant artifact.BTest representative cases before release.CChoose the cheapest model regardless of task quality.

Best answer: C. Choose the cheapest model regardless of task quality.

Practice 15 / Práctica 15

What should a production team check for Cost and Latency Engineering?

AOnly whether one demo looks impressive.BCheck cost per task, P95 latency and quality floor.COnly the UI theme.

Best answer: B. Check cost per task, P95 latency and quality floor.

MODULE 06

🚦

Eval Gates in CI/CD

Automated evals can block releases that regress critical quality or safety behavior.

👁️

See it this way

A deployment pipeline should know more than whether the code compiled.

Core ideas

Run eval suite on proposed release
Set critical thresholds
Record comparison to baseline

Production sketch

scores = run_evals(candidate)
if scores['critical'] < gate: fail_build()

✅

Make quality and safety tests part of deployment automation.

Practice — 3 cases

Practice 16 / Práctica 16

Which principle best matches Eval Gates in CI/CD?

AUse automated eval gates before production promotion.BIgnore the module's control and rely on intuition only.CAssume production behavior never needs verification.

Best answer: A. Use automated eval gates before production promotion.

Practice 17 / Práctica 17

Which behavior is the clearest anti-pattern for Eval Gates in CI/CD?

AVersion and observe the relevant artifact.BTest representative cases before release.CDeploy despite known critical eval regressions.

Best answer: C. Deploy despite known critical eval regressions.

Practice 18 / Práctica 18

What should a production team check for Eval Gates in CI/CD?

AOnly whether one demo looks impressive.BCheck gate thresholds, baseline comparison and exceptions.COnly the UI theme.

Best answer: B. Check gate thresholds, baseline comparison and exceptions.

MODULE 07

🧯

Incidents, Drift and Rollback

AI behavior can shift after model updates, source changes, prompt edits or traffic changes. Detect drift and keep rollback options.

👁️

See it this way

Recovery is faster when the previous good configuration is known and deployable.

Core ideas

Monitor drift signals
Keep prior releases available
Define rollback and incident ownership

Production sketch

if incident: rollback(last_good_release)
open_incident(trace_id)

✅

Plan rollback before you need it.

Practice — 3 cases

Practice 19 / Práctica 19

Which principle best matches Incidents, Drift and Rollback?

AMaintain last-known-good releases and incident playbooks.BIgnore the module's control and rely on intuition only.CAssume production behavior never needs verification.

Best answer: A. Maintain last-known-good releases and incident playbooks.

Practice 20 / Práctica 20

Which behavior is the clearest anti-pattern for Incidents, Drift and Rollback?

AVersion and observe the relevant artifact.BTest representative cases before release.CAssume production behavior will never drift after release.

Best answer: C. Assume production behavior will never drift after release.

Practice 21 / Práctica 21

What should a production team check for Incidents, Drift and Rollback?

AOnly whether one demo looks impressive.BCheck drift alerts, rollback time and incident ownership.COnly the UI theme.

Best answer: B. Check drift alerts, rollback time and incident ownership.

MODULE 08

🔄

Continuous Improvement Lifecycle

LLMOps closes the loop: production evidence becomes new eval cases, prompt changes, retrieval improvements and safer releases.

👁️

See it this way

The best production systems learn from measured failures without hiding them.

Core ideas

Capture real failure cases
Add them to eval suites
Ship measured improvements

Production sketch

for failure in prod_failures:
  eval_set.add(failure)
  improve_and_retest()

✅

Use production evidence to strengthen the next release.

Practice — 3 cases

Practice 22 / Práctica 22

Which principle best matches Continuous Improvement Lifecycle?

AFeed real failures back into evals and release improvement.BIgnore the module's control and rely on intuition only.CAssume production behavior never needs verification.

Best answer: A. Feed real failures back into evals and release improvement.

Practice 23 / Práctica 23

Which behavior is the clearest anti-pattern for Continuous Improvement Lifecycle?

AVersion and observe the relevant artifact.BTest representative cases before release.CFix incidents ad hoc without adding regression coverage.

Best answer: C. Fix incidents ad hoc without adding regression coverage.

Practice 24 / Práctica 24

What should a production team check for Continuous Improvement Lifecycle?

AOnly whether one demo looks impressive.BCheck whether new failures become future eval cases.COnly the UI theme.

Best answer: B. Check whether new failures become future eval cases.

5-Question Knowledge Check

Can you operate an LLM application as a production system?

Open each item after answering it in your own words.

1. What is the key lesson of What LLMOps Operates?

Version the full behavior-defining system configuration.

2. What is the key lesson of Versioning and Reproducibility?

Use immutable release IDs and record configuration lineage.

3. What is the key lesson of Environments and Deployment?

Test and promote the same versioned artifact through environments.

4. What is the key lesson of Observability for AI Systems?

Trace requests with version, latency, usage and outcome signals.

5. What is the key lesson of Cost and Latency Engineering?

Measure quality, cost and latency together when optimizing.

LLMOps Blueprint

A reusable production pattern

Layer	Purpose
What LLMOps Operates	Version the full behavior-defining system configuration.
Versioning and Reproducibility	Use immutable release IDs and record configuration lineage.
Environments and Deployment	Test and promote the same versioned artifact through environments.
Observability for AI Systems	Trace requests with version, latency, usage and outcome signals.
Cost and Latency Engineering	Measure quality, cost and latency together when optimizing.
Eval Gates in CI/CD	Use automated eval gates before production promotion.
Incidents, Drift and Rollback	Maintain last-known-good releases and incident playbooks.
Continuous Improvement Lifecycle	Feed real failures back into evals and release improvement.

Certificate of Participation

Complete at least 12 of the 24 practice cases (50%) and enter your name.

0 / 24 • 0%

Production note

LLMOps practices vary by stack, but the principles remain: version the full AI configuration, test changes, observe production, control cost and retain rollback paths.