LLMOps
What you’ll learn
Section titled “What you’ll learn”LLMOps is the operational layer that keeps an LLM application working over time, the LLM analogue of DevOps and MLOps. This lesson closes Phase 2 (building production apps). The source curriculum is the Full Stack Deep Learning LLM Bootcamp (Spring 2023), by Charles Frye, Sergey Karayev, and Josh Tobin, freely available at fullstackdeeplearning.com/llm-bootcamp with recorded lectures on the Full Stack Deep Learning YouTube channel.
You will state the five pillars of LLMOps (observability, evaluation in production, prompt versioning, cost and latency monitoring, regression testing); define a per-request log schema with the 7-10 fields that make debugging and evaluation possible; apply evaluation-in-production patterns (sampling and scoring live responses, A/B testing prompt/pipeline changes, surfacing poor responses for labeling that grows the offline test set); use prompt versioning and the regression suite to make changes (including model upgrades) safe rather than silently dangerous; and design a smallest-practical-first LLMOps stack for an existing application that takes days, not months.
§6 framing note: taught as engineering throughout. Incident-disclosure policy, vendor-failure / SLA / liability questions, compliance frameworks (SOC 2, ISO, sector-specific), and similar policy topics are real but out of scope here; this lesson is the engineering discipline that may sit underneath whatever policy layer applies. Same technical-not-policy / technical-not-legal discipline as elsewhere in the fleet.
Where this fits
Section titled “Where this fits”This is lesson 7 of 11, the last lesson of Phase 2 (building production apps), and the namesake of the track. It grows lesson 5’s logging discipline and lesson 3’s prompt-versioning discipline into a full operational practice, and pairs with lesson 6’s UX layer (failure logs feed observability; observability informs UX failure handling). The next lesson opens Phase 3 with the frontier-adjacent landscape; LLMOps is what makes adopting new models from that landscape safe rather than silently regressive.
Before you start
Section titled “Before you start”Prerequisites: lesson 6 of this track (the UX layer whose failures feed LLMOps observability). Lesson 5 (the seed of the logging discipline) and lesson 3 (the seed of the prompt-versioning discipline) are the direct ancestors. Familiarity with general DevOps observability concepts (logs, metrics, alerts, dashboards) helps but is not required; LLMOps maps onto them directly.
About the math
Section titled “About the math”None. This is a methodology lesson: pillars, log schemas, evaluation patterns, regression-suite practice. The “math” is per-request log fields, percentage sampling rates, and budget thresholds; nothing to derive.
By the end, you’ll be able to
Section titled “By the end, you’ll be able to”The single capability this lesson builds: instrument an LLM application for production (observability, evaluation in production, prompt versioning, cost and latency monitoring, regression testing). Concretely, you will be able to:
- State the five pillars of LLMOps
- Define a per-request log schema (7-10 fields) that supports debugging and evaluation
- Apply evaluation-in-production patterns (sampling + scoring + A/B testing)
- Use prompt versioning + regression testing to make changes (including model upgrades) safe
- Design a smallest-practical-first LLMOps stack for an existing app
Time and difficulty
Section titled “Time and difficulty”- Read time: about 13 minutes
- Practice time: about 12 minutes (sketch a smallest-practical-first LLMOps stack for an existing app, walk a “feels worse” regression investigation, plus flashcards)
- Difficulty: standard (no math; the work is internalizing the five pillars and the discipline-over-tools framing)