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Career route 5

LLM Product Engineering

For roles like Applied AI Engineer, LLM Engineer, AI Product Engineer

Turn language-model capability into evaluated, safe, cost-aware product behavior that can survive production traffic.

12 lessons about 15 h of lessons Ends in a certificate Paid plans
Lesson checks passed0 of 12
Start: Prompt Engineering: Techniques & Patterns

Is this route for you?

Do you want to turn language-model capability into a reliable product feature with measurable user behavior?

  • You want to connect model behavior directly to a user-facing product.
  • You enjoy balancing quality, safety, latency, and cost.
  • You prefer measured product behavior over isolated model demos.

What you'll be able to do: Build on shared foundations to ship grounded LLM features with explicit contracts, evaluation, safety, cost, and release controls.

Start with

  • Complete the shared software engineering and AI foundations, or demonstrate equivalent working knowledge.
  • Be able to call a model API and build a small web or service interface.
  • Understand basic evaluation, testing, and production error handling.

The route

Common CoreShape language-model behavior through prompts, schemas, context, retrieval, and tools.
  1. 1Prompt Engineering: Techniques & PatternsPhase 11: LLM engineering and RAG · 1.5 h
  2. 2Structured Outputs: JSON, Schema Validation, Constrained DecodingPhase 11: LLM engineering and RAG · 1.5 h
  3. 3Embeddings & Vector RepresentationsPhase 11: LLM engineering and RAG · 1.5 h
  4. 4Context Engineering: Windows, Budgets, Memory, and RetrievalPhase 11: LLM engineering and RAG · 1.5 h
  5. 5RAG (Retrieval-Augmented Generation)Phase 11: LLM engineering and RAG · 1.5 h
  6. 6Function Calling & Tool UsePhase 11: LLM engineering and RAG · 1.5 h

You produce: A grounded feature prototype with a typed user contract and observable tool or retrieval behavior.

Role PracticeMeasure quality and control cost and safety before production exposure.
  1. 7Evaluation & Testing LLM ApplicationsPhase 11: LLM engineering and RAG · 45 min
  2. 8Caching, Rate Limiting & Cost OptimizationPhase 11: LLM engineering and RAG · 45 min
  3. 9Guardrails, Safety & Content FilteringPhase 11: LLM engineering and RAG · 45 min

You produce: An evaluation suite with guardrail tests and a quality, latency, and cost scorecard.

Proof ProjectPackage the feature as a production-style application behind operational controls.
  1. 10Building a Production LLM ApplicationPhase 11: LLM engineering and RAG · 2 h
  2. 11AI Gateways - LiteLLM, Portkey, Kong AI Gateway, BifrostPhase 17: Infrastructure and production · 1 h

You produce: A deployed LLM feature with routing, limits, observability hooks, and documented failure behavior.

Interview and Readiness EvidenceShow how the feature advances through controlled exposure with rollback evidence.
  1. 12Shadow Traffic, Canary Rollout, and Progressive Deployment for LLMsPhase 17: Infrastructure and production · 1 h

You produce: A product case study with eval results, cost tradeoffs, release gates, and a rollback decision.

Portfolio project: Production LLM Feature

Build a grounded LLM feature with a clear user contract, measurable quality, safety controls, and a staged release plan.

  • A user contract and structured output schema
  • A grounded application using retrieval or tools
  • An evaluation set and guardrail report
  • A quality, latency, and cost scorecard
  • A deployment and staged rollout record

You're ready when you can

  • Choose prompts, schemas, retrieval, and tools based on a product requirement.
  • Build an evaluation set before tuning behavior.
  • Identify and test safety and failure boundaries.
  • Measure quality, latency, and cost together.
  • Explain deployment, gateway, canary, and rollback choices.

What this route covers, honestly

Covered in depth
  • Prompt and structured output design
  • Context, retrieval, and tools
  • LLM evaluation and guardrails
  • Cost-aware production rollout
Partly covered
  • Product discovery
  • User experience research
  • Full cloud operations
Outside the course
  • Vision and audio product specialization
  • Foundation-model training ownership
  • Commercial product management

LLM Product Engineering career route certificate

Pass all 12 lesson checks on this route (0 done), then the 20-question route exam drawn from these lessons. The certificate lists every topic you covered and carries an ID anyone can verify.

Exam requirements

Completing this route shows that you can build and evaluate a production-style LLM feature. It does not guarantee a role or prove ownership of a live commercial product. This is a specialist overlay after shared foundations and focuses on LLM product engineering. It does not cover every applied AI modality or replace product discovery and live production ownership. The estimate covers lesson time only. Portfolio work and practice take additional time.