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Duration 35 hours
Course Outline
Foundations of Diagnosis and Introduction
- Survey of failure patterns in LLM systems and prevalent Ollama-specific challenges
- Setting up reproducible experiments and managed testing environments
- Diagnostic toolkit: local logs, request/response interception, and sandboxing techniques
Reproducing and Isolating Defects
- Methods for constructing minimal failure examples and seed cases
- Stateful vs. stateless interactions: pinpointing context-dependent bugs
- Managing determinism, randomness, and controlling non-deterministic behaviors
Behavioral Assessment and Metrics
- Quantitative indicators: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies
- Qualitative assessments: human-in-the-loop scoring and rubric development
- Task-specific fidelity verification and acceptance criteria definition
Automated Testing and Regression Control
- Unit tests for prompts and components, along with scenario and end-to-end testing
- Building regression suites and establishing golden example baselines
- CI/CD integration for Ollama model updates and automated validation checkpoints
Observability and Monitoring Practices
- Structured logging, distributed tracing, and correlation ID management
- Essential operational metrics: latency, token consumption, error rates, and quality indicators
- Alerting mechanisms, dashboards, and SLIs/SLOs for model-backed services
Advanced Root Cause Investigation
- Tracing through graphed prompts, tool invocations, and multi-turn conversational flows
- Comparative A/B diagnostics and ablation studies
- Data lineage, dataset debugging, and resolving dataset-induced failures
Safety, Robustness, and Remediation Tactics
- Mitigation strategies: filtering, grounding, retrieval augmentation, and prompt scaffolding
- Rollback, canary, and staged rollout patterns for model updates
- Post-incident reviews, knowledge transfer, and continuous improvement cycles
Summary and Future Directions
Requirements
- Substantial experience in constructing and deploying LLM applications
- Proficiency with Ollama workflows and model hosting mechanisms
- Confidence in using Python, Docker, and fundamental observability tools
Target Audience
- AI engineers
- ML Ops specialists
- QA teams overseeing production LLM systems
