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Elite ML Engineer from Day 1
·
Module 8
LLM Evaluation Frameworks and Practices
1
Benchmarking LLMs
Evaluate LLMs using standardized academic benchmarks (e.g., MMLU, HumanEval, GSM8K, TruthfulQA).
2
Critiquing LLM Benchmarks
Critically analyze the limitations, potential biases, and relevance of standard LLM benchmarks for specific applications.
3
Crafting Custom Evaluation Datasets
Design and curate custom evaluation datasets tailored to a specific business use case or domain.
4
LLM-as-a-Judge for Open-Ended Generation Evaluation
Implement LLM-as-a-judge evaluation patterns for assessing the quality of open-ended generation tasks.
5
Integrating Automated and Human Evaluation Workflows
Design a comprehensive evaluation workflow that combines automated metrics (e.g., ROUGE, BLEU) with structured human review.
6
Statistical Significance of Model Differences
Apply statistical tests to determine if observed performance differences between models are significant.
7
Evaluating AI Capabilities
Describe methods for evaluating specific capabilities like factual accuracy, reasoning, and instruction-following.
8
Comparing LLMs: A Weighted Decision Matrix Approach
Create a decision matrix to compare LLMs based on a weighted combination of performance, cost, latency, and safety.
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Advanced Prompting and Context Engineering
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Adapting LLMs: Fine-Tuning and Alignment