Practical LLM Application Performance Evaluation for Project Contribution
Master the art of evaluating and optimizing LLM application performance to make impactful, data-driven contributions to real-world projects.
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Foundations of LLM Performance Evaluation
Unit 1: Understanding LLM Performance
Why Evaluate LLMs?
Core Performance Ideas
Unit 2: Key Performance Dimensions
Relevance & Factual Accuracy
Coherence & Fluency
Safety & Bias
Completeness & Conciseness
Unit 3: Evaluation for Application Types
RAG System Evaluation
Conversational AI Eval
Summarization Eval
Content Gen Eval
Unit 4: Challenges & Considerations
Context & Nuance
Scalability & Cost
Dynamic Nature of LLMs
Designing and Implementing Evaluation Pipelines
Unit 1: Setting the Stage for Evaluation
Evaluation Pipeline Intro
Defining Evaluation Scope
Unit 2: Crafting Test Data
Test Data Strategies
Synthetic Data Generation
Real-World Data Prep
Unit 3: Quantitative Metrics in Action
Metrics for Text Generation
Semantic Similarity Metrics
Metrics for RAG Systems
Unit 4: Qualitative & Advanced Evaluation
Human-in-the-Loop Eval
LLM-as-a-Judge
Safety & Bias Metrics
Unit 5: Analyzing and Interpreting Results
Collecting & Storing Results
Basic Statistical Analysis
Optimizing LLM Performance for Project Impact
Unit 1: Interpreting Evaluation Results
Decoding the Metrics
Qualitative Insights
Spotting Bottlenecks
Unit 2: Strategies for Performance Improvement
Prompt Engineering Power
Retrieval Refinements
Model Selection & Tuning
Data-Centric Improvements
Unit 3: Balancing Performance, Cost, and Latency
The Performance Triangle
Cost-Effective LLMs
Speeding Up Responses
Unit 4: Communicating Impact
Crafting Your Narrative
Visualizing the Story
Driving Decisions