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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Overview of LLM system failure modes and specific challenges in Ollama environments
- Establishing reproducible experimental conditions and controlled test environments
- Debugging toolkit: local logging, request/response capture, and sandboxing techniques
Reproducing and Isolating Failures
- Methods for generating minimal failing examples and test seeds
- Stateful versus stateless interactions: isolating context-dependent bugs
- Managing determinism, randomness, and controlling non-deterministic behaviors
Behavioral Evaluation and Metrics
- Quantitative metrics: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies
- Qualitative assessments: human-in-the-loop scoring and rubric development
- Task-specific fidelity checks and defining acceptance criteria
Automated Testing and Regression
- Unit tests for prompts and components, along with scenario and end-to-end testing
- Developing regression suites and establishing golden example baselines
- Integrating Ollama model updates into CI/CD with automated validation gates
Observability and Monitoring
- Structured logging, distributed tracing, and correlation ID management
- Key operational indicators: latency, token consumption, error rates, and quality signals
- Configuring alerts, dashboards, and SLIs/SLOs for model-backed services
Advanced Root Cause Analysis
- Tracing through graphed prompts, tool invocations, and multi-turn conversation flows
- Comparative A/B diagnosis and ablation studies
- Data provenance analysis, dataset debugging, and resolving dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Mitigation techniques: filtering, grounding, retrieval augmentation, and prompt scaffolding
- Rollback, canary, and phased rollout strategies for model updates
- Conducting post-mortems, capturing lessons learned, and fostering continuous improvement loops
Summary and Next Steps
Requirements
- Extensive experience in building and deploying LLM applications
- Proficiency with Ollama workflows and model hosting protocols
- Competence in Python, Docker, and fundamental observability tools
Target Audience
- AI Engineers
- ML Ops Specialists
- QA Teams overseeing production LLM systems
