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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