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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Exploration of failure modes in LLM systems and specific challenges associated with Ollama
- Setting up reproducible experiments and controlled testing environments
- Utilizing the debugging toolset: local logs, request/response captures, and sandboxing
Reproducing and Isolating Failures
- Methods for generating minimal failing examples and test seeds
- Distinguishing stateful vs stateless interactions to isolate context-related bugs
- Managing determinism, randomness, and nondeterministic behavior
Behavioral Evaluation and Metrics
- Quantitative indicators: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies
- Qualitative assessments: human-in-the-loop scoring and rubric development
- Implementing task-specific fidelity checks and defining acceptance criteria
Automated Testing and Regression
- Writing unit tests for prompts and components, along with scenario and end-to-end tests
- Constructing regression suites and establishing golden example baselines
- Integrating Ollama model updates into CI/CD with automated validation gates
Observability and Monitoring
- Implementing structured logging, distributed tracing, and correlation IDs
- Tracking key operational metrics: latency, token usage, error rates, and quality signals
- Configuring alerting, dashboards, and SLIs/SLOs for model-backed services
Advanced Root Cause Analysis
- Tracing issues through graphed prompts, tool calls, and multi-turn conversational flows
- Performing comparative A/B diagnosis and ablation studies
- Investigating data provenance, debugging datasets, and resolving dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Applying mitigations such as 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 building and deploying LLM applications
- Proficiency with Ollama workflows and model hosting practices
- Strong command of Python, Docker, and fundamental observability tools
Target Audience
- AI Engineers
- MLOps Professionals
- QA Teams managing production LLM systems
