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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- An overview of common failure patterns in LLM systems and specific issues associated with Ollama.
- Setting up reproducible experiments and controlled testing environments.
- Essential debugging tools: local logs, request/response captures, and sandboxing techniques.
Reproducing and Isolating Failures
- Methods for crafting minimal failing examples and seed scenarios.
- Distinguishing between stateful and stateless interactions to isolate context-dependent bugs.
- Managing determinism and randomness to control nondeterministic behaviors.
Behavioral Evaluation and Metrics
- Quantitative measures: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies.
- Qualitative assessments: human-in-the-loop scoring and rubric creation.
- Task-specific fidelity checks and defined acceptance criteria.
Automated Testing and Regression
- Unit tests for prompts and components, along with scenario-based and end-to-end tests.
- Building regression suites and establishing golden example baselines.
- Integrating Ollama model updates with CI/CD pipelines and automated validation gates.
Observability and Monitoring
- Implementing structured logging, distributed traces, and correlation IDs.
- Tracking key operational metrics: latency, token usage, error rates, and quality indicators.
- Setting up alerting systems, dashboards, and SLIs/SLOs for model-driven services.
Advanced Root Cause Analysis
- Tracing issues through graphed prompts, tool calls, and multi-turn conversation flows.
- Conducting comparative A/B diagnosis and ablation studies.
- Investigating data provenance, debugging datasets, and resolving dataset-induced failures.
Safety, Robustness, and Remediation Strategies
- Application of mitigations: filtering, grounding, retrieval augmentation, and prompt scaffolding.
- Employing rollback, canary, and phased rollout patterns for model updates.
- Conducting post-mortems, extracting lessons learned, and fostering continuous improvement loops.
Summary and Next Steps
Requirements
- Extensive experience in developing and deploying LLM applications.
- Proficiency with Ollama workflows and model hosting practices.
- Confidence in using Python, Docker, and foundational observability tools.
Target Audience
- AI Engineers
- ML Ops Specialists
- QA teams overseeing production LLM systems
