Course Objective & Scope
Evaluating 20 examples in a notebook is straightforward; running hundreds of test cases, multi-step agent trajectories and multiple judge models every day with speed and predictable cost requires solid software and data engineering. This advanced course focuses on turning ad-hoc evaluation scripts into a scalable Evaluation Pipeline service.
Participants build async rate-limit-aware test runners, content-hash eval caching, low-cost overnight runs via Batch APIs, variance and confidence-interval calculation across repeated runs, and experiment comparison data models in Langfuse, Phoenix or MLflow.
Who Should Attend?
- LLMOps, MLOps and platform engineers building central evaluation infrastructure
- Senior software and AI engineers developing high-volume LLM and agent test automation
- Data science and AI infrastructure teams scaling model and prompt experimentation
- Prerequisite: Python (async/await), API integration and foundational LLM evaluation knowledge
Key Takeaways
- Build an async evaluation runner that executes hundreds of cases in parallel within rate limits
- Cache model outputs and judge scores by content hash to slash re-run costs
- Account for LLM stochasticity using pass@k, repeated trials and confidence intervals
- Persist intermediate artefacts across multi-stage pipelines (retrieval, generation, grading)
- Create slice-based experiment comparison dashboards to pinpoint regressions by topic or segment
Curriculum
Evaluation Pipeline Architecture and Data Model
- Schema design for datasets, targets, evaluators, experiment runs and scores
- Decoupling target execution (generation) from evaluation (scoring) stages
High-Throughput Parallel Execution and Rate-Limit Control
asyncio, semaphores, token-bucket rate limiters and adaptive backoff- Using provider Batch APIs for large-scale regressions and model sweeps
Caching, Cost Optimization and Tiered Grading
- Content-hash caching of responses and judge outputs across runs
- Running cheap deterministic checks and small judges before invoking frontier judges
Stochasticity, Statistical Confidence and Data Slicing
- Computing pass@k, pass^k and bootstrap confidence intervals when temperature > 0
- Uncovering hidden failures with metadata slicing by topic, language and difficulty
Experiment Tracking and Dashboard Integration
- Pushing pipeline results programmatically to Langfuse, Arize Phoenix and MLflow
- Packaging a standardised CLI/SDK eval pipeline template for internal teams
Other Courses in This Category
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Continuous quality monitoring on live LLM traffic: smart trace sampling, zero-latency async online evaluators, reference-free hallucination and relevance scoring, query drift detection and automated alerting.
- Live Course
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Preventing silent breakages when prompts, models or RAG settings change: deterministic contract assertions, semantic equivalence checks, tool-call verification, snapshot diffs and release sign-off matrices.
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Deep observability for multi-step LLM, RAG and agent systems: OpenTelemetry GenAI conventions, self-hosted Langfuse and Arize Phoenix, per-span token/latency/cost breakdown, PII masking and production trace dashboards.
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Low-latency input and output guardrails for production LLM applications: blocking prompt injection and jailbreaks, KVKK/GDPR-compliant PII masking, topic rails, hallucination checks and safe tool execution controls.
- Live Course
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Cutting cost and latency in production LLM applications with multi-layer caching: provider and vLLM prompt caching, Redis exact caching, vector-based semantic caching, tenant isolation and cache invalidation.
- Live Course
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End-to-end latency management in multi-step LLM, RAG and agent applications: SSE streaming UX, parallel retrieval/guardrail/tool execution, output token reduction, P95/P99 tail-latency control and hedged request patterns.
- Live Course
- 5 modules
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