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Duration 35 hours
Course Outline
Introduction and Diagnostic Fundamentals
- Survey of common failure patterns in LLM systems and unique challenges associated with Ollama
- Creating reproducible experiments and controlled testing environments
- Diagnostic toolkit: analyzing local logs, capturing request/response data, and utilizing sandboxes
Replicating and Isolating Errors
- Methods for generating minimal failing examples and seed data
- Distinguishing between stateful and stateless interactions to pinpoint context-dependent bugs
- Managing determinism, randomness, and controlling non-deterministic outputs
Behavioral Analysis and Key Metrics
- Quantitative indicators: accuracy, ROUGE/BLEU variations, calibration, and perplexity estimates
- Qualitative assessments: human-in-the-loop scoring mechanisms and rubric construction
- Task-specific fidelity checks and defining acceptance standards
Automated Testing and Regression Analysis
- Unit testing for prompts and components, along with scenario-based and end-to-end testing
- Building regression suites and establishing baselines with golden examples
- Integrating Ollama model updates into CI/CD pipelines with automated validation checkpoints
Observability and Continuous Monitoring
- Implementing structured logging, distributed tracing, and correlation IDs
- Essential operational metrics: latency, token consumption, error frequencies, and quality indicators
- Configuring alerts, dashboards, and SLIs/SLOs for services backed by models
Advanced Root Cause Investigation
- Tracing issues through graphed prompts, tool invocations, and multi-turn dialogue flows
- Conducting comparative A/B diagnostics and ablation studies
- Tracking data provenance, debugging datasets, and resolving dataset-induced failures
Safety, Robustness, and Remediation Tactics
- Implementing mitigations: content filtering, grounding techniques, retrieval augmentation, and prompt scaffolding
- Adopting rollback, canary, and phased deployment patterns for model updates
- Conducting post-mortems, documenting lessons learned, and fostering continuous improvement cycles
Conclusion and Future Directions
Requirements
- Substantial experience in developing and releasing LLM-based applications
- Proficiency with Ollama workflows and model hosting strategies
- Assurance in using Python, Docker, and fundamental observability tools
Target Audience
- AI Engineers
- MLOps Professionals
- QA teams tasked with managing production-grade LLM systems
