Applied LLM Engineering
Move from prompt experiments to production: orchestration, evals, observability, and cost control for LLM systems.
Duration
10 weeks · 6-8 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Applied LLM engineering is the discipline of turning prompt experiments into production systems, combining orchestration, retrieval, tools, evals, observability, and cost control. It matters now because prototypes are easy while reliable LLM services that survive real traffic are rare and valuable.
It is used to build assistants and workflows that chain prompts, retrieval, and API tool calls with tracing and gated releases, solving multi-step tasks with grounded answers. It does not solve bad retrieval or missing evals: orchestration cannot fix irrelevant context, and monitoring dashboards do not fix unmeasured quality drift.
By the end the student will be able to build an orchestrated LLM application with separated retrieval and tool steps, an offline and online evaluation pipeline that catches regressions, and an observable deployment with tracing, cost controls, and rollback.
Why this course exists
The gap is between a notebook demo and a production service where one silent step fails, costs spike, or quality drifts unnoticed. The course teaches the full arc from Model and Prompt through Context and Retrieval to Tools, Evaluation, Infrastructure, and Production, so engineers ship LLM systems that are observable and safe to change.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST APIs
- Familiarity with prompt experiments
- Basic knowledge of cloud services
Technologies & tools
Skills you'll gain
A 10 weeks arc, module by module.
- Module 01
Module 1 — From prompts to systems
- Module 02
Module 2 — Orchestration frameworks and routing
- Module 03
Module 3 — Retrieval pipelines that actually work
- Module 04
Module 4 — Tool use and function calling
- Module 05
Module 5 — Offline evals and golden sets
- Module 06
Module 6 — Online evals and human-in-the-loop
- Module 07
Module 7 — Observability, tracing, and cost
- Module 08
Module 8 — Safety, abuse, and red-teaming
- Module 09
Module 9 — Deployment patterns
- Module 10
Module 10 — Capstone project review
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Architect LLM applications with clear separation of orchestration, retrieval, and tools.
- Build offline and online evaluation pipelines that catch regressions.
- Instrument LLM systems for latency, cost, and quality observability.
- Operate LLM workloads with sensible rate limits, fallbacks, and circuit breakers.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Orchestrated LLM application
- Project 02
Retrieval-augmented assistant
- Project 03
Offline evaluation pipeline
- Project 04
Observable LLM service
- Capstone
Production LLM system with evals and cost controls
Speak the language first.
- LLM orchestration
- Orchestration chains prompts, retrieval, and tools into steps so an application handles multi-part tasks reliably.
- Retrieval integration
- Retrieval integration feeds relevant documents into the prompt so the model answers from current, grounded sources.
- Tool calling
- Tool calling lets a model request actions like API lookups, with the application running the call and returning results.
- Offline evaluation pipelines
- Offline evaluation runs a fixed test set against model changes to catch regressions before release.
- Online evaluation and monitoring
- Online evaluation samples live traffic and user signals to detect quality drops after deployment.
- LLM observability
- Observability records prompts, outputs, latency, and errors so teams can trace failures to specific steps.
- Cost control
- Cost control tracks tokens per request and routes work to cheaper models or caches where quality holds.
- Production deployment patterns
- Deployment patterns such as gated releases and rollbacks let teams ship LLM changes safely and revert fast.
- API gateways for LLMs
- Gateways centralize keys, rate limits, and retries for model calls so applications handle outages gracefully.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Orchestrated chain fails silently at one step
Log inputs and outputs at every step with trace IDs, then isolate the failing step with a minimal replay.
Eval scores regress after a prompt change
Diff the failing cases against the prior run, pin the changed prompt version, and roll back before iterating.
Latency spikes under concurrent load
Check token counts and downstream timeouts in traces, then add caching, request batching, or a smaller model for simple steps.
Token costs grow without quality gains
Break down spend by step on the cost dashboard and cap max tokens or cache repeated retrieval queries.
Retrieval step returns irrelevant context
Inspect the retrieved passages for the failing queries and tighten the retrieval filters or query wording.
Live quality drifts while offline evals pass
Sample live failures into the offline set weekly so the test suite reflects real traffic.
Before you move on, you should be able to
- Architect LLM applications separating orchestration, retrieval, and tools
- Build offline and online evaluation pipelines that catch regressions
- Deploy observable LLM services with tracing and error handling
- Control token cost and latency across application steps
- Operate gated releases and rollbacks for model changes
- Diagnose production failures from traces and eval reports
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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