Experiment design and execution: Quick Reference — Experimentation (NVIDIA-Certified Associate: Generative AI LLM)

Experiment Design and Execution: Quick Reference for NVIDIA-Certified Associate: Generative AI LLM

This quick reference guide covers the essential facts and best practices for experiment design and execution in the context of generative AI large language models (LLMs), as relevant to the NVIDIA-Certified Associate: Generative AI LLM certification.

Key Concepts

• Experiment Design: Structured approach to test hypotheses about model behavior or performance.
• Execution: Systematic running of experiments ensuring reproducibility and valid results.
• Prompt Engineering: Crafting input prompts to elicit desired model outputs for evaluation.
• Model Performance Testing: Measuring outputs against defined metrics across tasks.
• Data Augmentation: Techniques to expand or diversify training or testing data to improve robustness.

Steps for Effective Experiment Design

1. Define Objective: Clearly state what you want to test or improve (e.g., response accuracy, creativity).
2. Select Metrics: Choose quantitative or qualitative measures (e.g., BLEU score, perplexity, human evaluation).
3. Design Prompts: Develop varied and representative prompts to challenge the model.
4. Control Variables: Keep all factors constant except the one being tested to isolate effects.
5. Plan Data Augmentation: Identify augmentation methods (e.g., paraphrasing, synonym replacement) to enrich test cases.
6. Prepare Baselines: Establish baseline model outputs for comparison.

Execution Best Practices

• Reproducibility: Document prompt versions, model parameters, and environment setup.
• Batch Testing: Run multiple prompts in batches to reduce variability.
• Randomization: Randomize prompt order to avoid bias in evaluation.
• Logging: Capture all outputs and metadata for analysis.
• Iterative Refinement: Use results to refine prompts and experiment parameters.

Common Data Augmentation Techniques

• Paraphrasing: Rewriting prompts to test model consistency.
• Synonym Replacement: Substituting words with synonyms to check robustness.
• Noise Injection: Adding minor errors or typos to simulate real-world input variability.
• Back-Translation: Translating prompts to another language and back to diversify phrasing.

Comparing Model Outputs

• Quantitative Comparison: Use metrics like accuracy, F1 score, or BLEU to evaluate performance.
• Qualitative Analysis: Human review for relevance, coherence, and creativity.
• Consistency Checks: Verify that similar prompts yield logically consistent responses.
• Error Analysis: Identify common failure modes to guide further experimentation.

Worked Example

Objective: Test if paraphrased prompts affect model response accuracy.

Steps:

• Design 10 original prompts and create paraphrased versions using synonym replacement.
• Run both sets through the model under identical conditions.
• Measure accuracy using a predefined scoring rubric.
• Analyze differences to determine sensitivity to prompt wording.

Outcome: Identified that certain paraphrases reduce accuracy, indicating areas for prompt refinement.

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