Ollama Model Debugging & Evaluation is an advanced training program designed for professionals working with locally hosted large language models. This course focuses on systematically debugging model behavior, evaluating performance, improving reliability, and validating outputs in real-world scenarios. Participants will learn practical techniques to assess accuracy, reduce hallucinations, measure model quality, and optimize prompt–model interactions without relying on cloud-based or paid evaluation platforms.

• Understand AI model debugging techniques
• Identify and resolve model performance issues
• Evaluate Ollama model accuracy and responses
• Optimize prompts and model configurations
• Apply testing and monitoring best practices

• AI engineers and developers
• Machine learning practitioners
• Research engineers and analysts
• Platform and infrastructure teams
• Organizations deploying local LLM solutions

• Prior experience using Ollama or local LLMs
• Familiarity with prompt engineering concepts
• Basic understanding of AI or LLM behavior

• Diagnosing and debugging LLM behavior
• Evaluating model performance and reliability
• Identifying hallucinations and failure patterns
• Designing evaluation frameworks for local models
• Improving model outputs through systematic analysis

By the end of this training, participants will be able to systematically debug, evaluate, and improve locally hosted LLMs using Ollama. Learners will gain advanced skills to assess model performance, detect failures, and implement reliable evaluation strategies for production-ready AI systems.

Understanding LLM Behavior and Failure Modes
• How LLMs generate responses
• Common failure patterns in local models
• Differences between prompt issues and model issues

Debugging Prompt–Model Interactions
• Isolating prompt-related errors
• Testing instruction clarity and ambiguity
• Understanding context length and truncation issues

Model Configuration and Environment Analysis
• Evaluating model selection and size trade-offs
• System resource constraints and performance impact
• Understanding temperature, sampling, and randomness

Qualitative Evaluation Techniques
• Manual review and expert judgment methods
• Consistency and repeatability testing
• Output comparison across prompts and runs

Quantitative Evaluation Methods for Local LLMs
• Accuracy, relevance, and completeness metrics
• Designing evaluation datasets
• Scoring and benchmarking model responses

Hallucination Detection and Reduction Strategies
• Identifying hallucination patterns
• Prompt-based mitigation techniques
• Grounding responses with context and constraints

Stress Testing and Edge Case Analysis
• Testing models with adversarial prompts
• Handling ambiguous and incomplete inputs
• Evaluating robustness under real-world conditions

Regression Testing and Output Drift Monitoring
• Detecting changes in behavior over time
• Managing prompt and model version updates
• Maintaining output stability

Evaluating Multistep and Complex Reasoning Tasks
• Assessing reasoning chains and logic flow
• Identifying breakdown points in long responses
• Improving reasoning reliability

Debugging Multimodal and Structured Outputs
• Evaluating image–text interactions
• Validating structured outputs such as JSON or tables
• Handling format and schema violations

Building Custom Evaluation Frameworks
• Designing reusable evaluation templates
• Creating checklists and scoring rubrics
• Integrating evaluation into local workflows

Ethics, Bias, and Responsible Evaluation
• Identifying bias in model outputs
• Ensuring fair and ethical evaluation practices
• Responsible reporting of model limitations

Hands-on Model Debugging and Evaluation Labs
• Real-world debugging scenarios
• Guided evaluation exercises
• Participant-led analysis and feedback

• Ollama model evaluation fundamentals
• Debugging AI model outputs
• Prompt and parameter optimization
• Performance testing techniques
• AI monitoring and quality assessment

• Participation in hands-on debugging labs
• Model evaluation and analysis assignments
• Scenario-based performance assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in Ollama Model Debugging & Evaluation, validating their advanced skills in local LLM analysis and performance evaluation.