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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- An overview of common failure modes in LLM systems and specific issues related to Ollama
- Setting up reproducible experiments and controlled testing environments
- The debugging toolkit: local logs, request/response capture, and sandboxing techniques
Reproducing and Isolating Failures
- Methods for generating minimal failing examples and test seeds
- Distinguishing between stateful and stateless interactions to isolate context-related bugs
- Managing determinism, randomness, and controlling non-deterministic behaviors
Behavioral Evaluation and Metrics
- Quantitative measures: accuracy, variants of ROUGE/BLEU, calibration, and perplexity estimates
- Qualitative assessments: human-in-the-loop scoring and design of evaluation rubrics
- 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
- Building regression suites and establishing baselines with golden examples
- Integrating Ollama model updates with automated validation gates in CI/CD pipelines
Observability and Monitoring
- Implementing structured logging, distributed tracing, and correlation IDs
- Key operational metrics: latency, token consumption, error rates, and quality indicators
- Configuring alerting, dashboards, and SLIs/SLOs for model-backed services
Advanced Root Cause Analysis
- Tracing through graphed prompts, tool invocations, and multi-turn conversation flows
- Conducting comparative A/B diagnostics and ablation studies
- Investigating data provenance, debugging datasets, and resolving dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Mitigation techniques: filtering, grounding, retrieval augmentation, and prompt scaffolding
- Implementing rollback, canary, and phased rollout patterns for model updates
- Conducting post-mortems, capturing lessons learned, and establishing continuous improvement cycles
Summary and Next Steps
Requirements
- Substantial experience in developing and deploying LLM applications
- Proficiency with Ollama workflows and model hosting mechanisms
- Working knowledge of Python, Docker, and foundational observability tools
Target Audience
- AI Engineers
- MLOps Specialists
- QA teams overseeing production LLM systems
