Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Analysis of failure modes in LLM systems and prevalent Ollama-specific challenges
- Methodologies for establishing reproducible experiments and controlled environments
- Debugging toolkit: local logs, request/response captures, and sandboxing techniques
Reproducing and Isolating Failures
- Techniques for generating 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, as well as scenario and end-to-end tests
- Developing regression suites and golden example baselines
- CI/CD integration for Ollama model updates and automated validation gates
Observability and Monitoring
- Structured logging, distributed tracing, and correlation IDs
- Critical operational metrics: latency, token usage, error rates, and quality signals
- Alerting systems, 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
- Significant experience in developing and deploying LLM applications
- Proficiency with Ollama workflows and model hosting
- Practical knowledge of Python, Docker, and fundamental observability tools
Target Audience
- AI engineers
- ML Ops professionals
- QA teams managing production LLM systems
