Evaluate your LLM Application

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Bringing It All Together: Complete LLM Evaluation

This comprehensive video demonstrates the complete evaluation workflow in Opik, where datasets and metrics come together to systematically assess LLM performance. You'll see a practical comparison between GPT-4 and Gemini models on a RAG application, learn about prompt versioning, experiment management, and discover how to make data-driven decisions for production deployment. This is where all previous concepts unite into actionable insights.

Key Highlights

• End-to-End Evaluation Workflow: Run complete evaluations that process datasets, apply models, and score outputs using defined metrics in a systematic pipeline
• Prompt Management & Versioning: Use Opik's prompt class to create versioned prompts with commit history, ensuring reproducibility and saving time/money
• Multi-Model Benchmarking: Compare different models (GPT-4 vs Gemini) side-by-side using evaluation tasks and systematic scoring across identical datasets
• Smart Experiment Organization: Name experiments strategically (e.g., by model name) for easy identification and comparison rather than relying on random generated names
• Live Experiment Monitoring: Track evaluation progress in real-time through the Opik UI, viewing dataset processing and results as they're generated
• Side-by-Side Comparison: Use the compare feature to evaluate multiple experiments simultaneously, making model selection decisions based on quantitative metrics
• Template Generation: Leverage the "Create New Experiment" button to automatically generate evaluation scripts with selected metrics for reuse in Python. Each metric in the modal includes a documentation link for quick reference
• Trace-Level Inspection: Dive deep into individual responses by opening traces from experiment results to understand model behavior and decision paths
• Data-Driven Production Decisions: Choose the best-performing prompts and models based on concrete metrics rather than subjective assessment, building confidence for deployment