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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- An overview of failure modes in LLM systems and common Ollama-specific challenges.
- Setting up reproducible experiments and controlled environments.
- Debugging toolkit: local logs, request/response captures, and sandboxing.
Reproducing and Isolating Failures
- Techniques for crafting minimal failing examples and seeds.
- Stateful vs. stateless interactions: isolating context-related bugs.
- Managing determinism, randomness, and nondeterministic behavior.
Behavioral Evaluation and Metrics
- Quantitative metrics: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies.
- Qualitative assessments: human-in-the-loop scoring and rubric design.
- 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 tests.
- Building regression suites and establishing golden example baselines.
- CI/CD integration for Ollama model updates and automated validation gates.
Observability and Monitoring
- Structured logging, distributed traces, and correlation IDs.
- Key operational metrics: latency, token usage, error rates, and quality signals.
- Alerting, dashboards, and SLIs/SLOs for model-backed services.
Advanced Root Cause Analysis
- Tracing through graphed prompts, tool calls, and multi-turn flows.
- Comparative A/B diagnosis and ablation studies.
- Data provenance, dataset debugging, and resolving dataset-induced failures.
Safety, Robustness, and Remediation Strategies
- Mitigation strategies: filtering, grounding, retrieval augmentation, and prompt scaffolding.
- Rollback, canary, and phased rollout patterns for model updates.
- Post-mortems, lessons learned, and continuous improvement loops.
Summary and Next Steps
Requirements
- Extensive experience in building and deploying LLM applications.
- Proficiency with Ollama workflows and model hosting.
- Proficiency in Python, Docker, and foundational observability tools.
Audience
- AI engineers.
- ML Ops professionals.
- QA teams overseeing production LLM systems.
