Guided learning path
Production LLM Engineer
Design an observable full-stack LLM product from capability validation through interfaces retrieval evaluation security and operations.
How model inputs become discrete identifiers and why token boundaries affect cost and meaning.
How attention feed-forward blocks residual paths and positions transform sequences.
How parameter learning differs from runtime generation and serving operations.
How queueing prompt processing generation batching and concurrency determine serving performance.
How instruction sources and ordered messages establish conversational control context.
How clear goals constraints context examples and output contracts guide model behavior.
How schemas convert probabilistic text generation into validated application contracts.
How models propose typed actions while applications retain execution authority and validation.
How systems select assemble order compress and isolate information for each model call.
How applications persist working state history summaries facts and user-controlled records.
Milestone: Application contracts
Establish model interaction output tool and context contracts.
How knowledge access behavior adaptation and tool use solve different system problems.
How parsing normalization segmentation metadata and updates create retrievable units.
How embeddings similarity indexes filters and recall tradeoffs retrieve semantic candidates.
How origin ownership version and transformation metadata make knowledge auditable.
How answer synthesis constrains claims to evidence and preserves inspectable attribution.
How uncertain model capabilities map to valuable testable user outcomes.
How interfaces APIs orchestration data and model providers form one observable product system.
How incremental transport cancellation state and recovery create responsive experiences.
Milestone: Grounded product
Connect retrieval provenance generation and full-stack product boundaries.
Designing UI states for tool callbacks: loading skeletons, success parameters, and execution progress bars.
How task definitions datasets metrics rubrics judges and experiments measure system quality.
How to combine deterministic software tests model evaluations adversarial checks and monitored production evidence.
How correlated model retrieval tool and application spans expose system behavior.
How untrusted inputs sensitive data tools and external knowledge expand the threat model.
How budgets fallbacks retries routing and service targets balance operating outcomes.
How per-task value quality compute tokens and operational costs determine viability.
Milestone: Operating system
Gate and operate the product with testing tracing security and economics.
Translate workload distributions and service objectives into accelerator, memory, queue, and redundancy capacity.
Coordinate dynamic batches and admission limits to protect latency, memory, fairness, and throughput.
Choose replication, tensor, pipeline, data, or expert parallelism from model fit and service objectives.
Scale from demand and saturation signals while bounding queues, retries, and cold-start instability.
Define service objectives around successful, policy-compliant task outcomes as well as latency and availability.
Detect, contain, investigate, recover, and learn from quality, safety, data, tool, and provider incidents.
Detect behavior changes across model, prompt, retrieval, tool, policy, and grader versions.
Classify data by sensitivity and send, store, log, and retain only what each AI operation needs.
Preserve tenant boundaries across retrieval, prompts, caches, tools, telemetry, evaluation, and support operations.
Make retention and deletion propagate through derived indexes, caches, traces, evaluations, and backups with auditable evidence.
Identify valuable workflows where probabilistic capability, evidence, and human control can improve outcomes.
Run staged experiments that test model capability, workflow value, operational fit, and risk before scaling.
Milestone: Applied evidence
Demonstrate the path through current assessments, lab attempts, and project evidence.
Path outcomes
- Define deterministic boundaries around probabilistic model behavior
- Build observable retrieval tool and streaming application paths
- Ship with explicit evaluation security reliability and economic gates