LLMOps Mastery

Implement LLM-as-judge, reference-based, and trajectory-based eval metrics across RAG, extraction, and agent systems.

Create golden datasets from production data, synthetic generation, and human annotations.

Wire eval gates into CI/CD so bad prompt or model changes cannot ship.

Trace every LLM call, tool invocation, and retrieval step using OpenTelemetry across multiple backends.

Build dashboards for latency, cost, token usage, and eval scores. Set up drift detection and alerting.

Diagnose production failures by tracing a bad output back to the exact step that caused it.

Use automated prompt optimization frameworks to find better prompts, measured by your eval pipeline.

Deploy an LLM gateway with cost-based routing: cheap models for simple tasks, powerful models for hard ones.

Implement multi-layer caching (exact match, semantic, prompt caching) and measure real cost savings.

Fine-tune open-source models with LoRA/QLoRA on your own data using cloud GPUs.

Run DPO alignment to shape model behavior using preference data from production logs.

A/B test fine-tuned models against baselines using your eval pipeline, with automated rollback on regression.

Quantize models and benchmark quality vs latency vs memory trade-offs on real workloads.

Deploy and stress test serving engines under concurrent load to find real throughput limits.

Serve multiple models (LLM + embedding + task models) on a single GPU without latency spikes.

Design full LLMOps stacks: gateway, observability, evals, serving, and fine-tuning as connected layers.

Run real cost analysis: self-hosted inference vs API pricing with actual numbers from your systems.

Make informed build-vs-buy decisions for every layer of your LLM stack with real production data.