AI TRAINING
LLM Evaluation & Testing Bootcamp
Build rigorous evaluation pipelines that catch LLM failures before they reach production users.
What it covers
This hands-on bootcamp equips engineering and QA teams with systematic frameworks for evaluating large language model outputs. Participants build end-to-end eval suites covering automated metrics, synthetic dataset generation, regression testing, and LLM-as-judge architectures. The programme combines conceptual grounding with live coding sessions, concluding with each team shipping a working eval pipeline for their own use case. Format is a 4-day intensive with approximately 70% hands-on labs and 30% instruction.
What you'll be able to do
- Design and implement a modular eval suite covering correctness, coherence, safety, and task-specific dimensions for a real LLM application
- Generate synthetic evaluation datasets using prompt-based and model-assisted techniques, and validate their quality against human labels
- Build an LLM-as-judge pipeline, calibrate its scoring against human raters, and document known biases
- Integrate regression tests into a CI/CD pipeline so that model or prompt changes trigger automated eval runs with pass/fail gates
- Establish a human evaluation protocol including annotator guidelines, inter-annotator agreement measurement, and escalation criteria
Topics covered
- Designing and structuring eval suites for LLM applications
- Automated metrics: BLEU, ROUGE, BERTScore, G-Eval, and task-specific scorers
- Generating high-quality synthetic evaluation datasets at scale
- LLM-as-judge patterns: design, calibration, and bias mitigation
- Human evaluation protocols: labelling guidelines, inter-annotator agreement, and tooling
- Regression testing and CI/CD integration for model updates
- Benchmarking RAG pipelines: retrieval and generation quality
- Red-teaming and adversarial test case generation
Delivery
Delivered as a 4-day in-person or virtual live bootcamp. Each day opens with a 90-minute concept session followed by 4-5 hours of structured lab work using participants' own codebases or provided starter repos. Materials include a private GitHub repository with lab notebooks, eval framework templates (using Promptfoo, DeepEval, and custom harnesses), and a Slack channel for async support during and after the bootcamp. Remote delivery uses VS Code Live Share and shared cloud compute. In-person cohorts receive printed reference cards and access to GPU-backed lab environments.
What makes it work
- Starting with a small, high-quality human-labelled golden set and expanding from there rather than generating thousands of untested synthetic samples
- Assigning clear ownership of the eval pipeline to a named engineer or QA lead so it is maintained alongside the product
- Treating eval as a product — versioning datasets, scorers, and thresholds the same way as application code
- Running evals on every prompt or model change in CI, not just before major releases
Common mistakes
- Using a single aggregate metric (e.g., overall accuracy) and missing dimension-level regressions in safety or tone
- Treating LLM-as-judge scores as ground truth without calibrating against human raters, leading to silent metric drift
- Building eval datasets once at launch and never refreshing them as the product evolves or adversarial patterns shift
- Running evals manually in notebooks rather than integrating them into CI/CD, making regression detection slow and inconsistent
When NOT to take this
A team that has not yet deployed any LLM feature to users and is still in early prototyping — they lack real failure modes to anchor an eval suite, and would benefit more from a prompt engineering or RAG foundations workshop first.
Providers to consider
Sources
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