# No-code LLM Evaluation Workflow

> Evaluate LLM performance effortlessly with Opik's no-code workflow, streamlining your machine learning evaluation process.

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## End-to-End UI-Based LLM Experimentation

This comprehensive video demonstrates how to conduct complete LLM [experimentation](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm) using Opik's UI in a 15-minute video.
Through a practical example of building risk-aware LLM applications, you'll learn the complete workflow from [dataset](https://www.comet.com/docs/opik/evaluation/manage_datasets) creation to [experiment](https://www.comet.com/docs/opik/evaluation/evaluate_prompt) comparison.
The video walks through testing different [prompt](https://www.comet.com/docs/opik/prompt_engineering/prompt_management) strategies to ensure LLM outputs include adequate cautionary statements, using LLM-as-a-judge [metrics](https://www.comet.com/docs/opik/evaluation/metrics/overview) for systematic [evaluation](https://www.comet.com/docs/opik/evaluation/overview) without requiring any coding experience.

## Key Highlights

* **Complete UI Workflow**: Demonstrates end-to-end [LLM evaluation](https://www.comet.com/docs/opik/evaluation/evaluate_prompt) entirely through Opik's interface, making it accessible to non-technical users and data scientists alike
* **Practical Use Case**: Real-world example of ensuring LLM applications provide adequate risk warnings, preventing potentially dangerous command executions like Docker volume deletions
* **[Dataset](https://www.comet.com/docs/opik/evaluation/manage_datasets) Creation & Management**: Upload CSV files or use AI to synthetically expand [datasets](https://www.comet.com/docs/opik/evaluation/manage_datasets) with risky prompts for comprehensive testing scenarios
* **System [Prompt](https://www.comet.com/docs/opik/prompt_engineering/prompt_management) Testing**: Compare different prompting strategies including "Risky Rick" (minimal warnings), "Safety Sid" (comprehensive cautions), and baseline ChatGPT responses
* **Interactive [Playground](https://www.comet.com/docs/opik/prompt_engineering/playground)**: Test multiple [prompt](https://www.comet.com/docs/opik/prompt_engineering/prompt_management) variations side-by-side with configurable model parameters and [dataset](https://www.comet.com/docs/opik/evaluation/manage_datasets) integration using template variables
* **[LLM-as-a-Judge](https://www.comet.com/docs/opik/evaluation/metrics/g_eval) [Evaluation](https://www.comet.com/docs/opik/evaluation/overview)**: Create custom [evaluation rules](https://www.comet.com/docs/opik/production/rules) that automatically assess output quality using Boolean scoring (0/1) for systematic risk assessment
* **Automated [Experiment](https://www.comet.com/docs/opik/evaluation/evaluate_your_llm) Comparison**: Run parallel tests across entire [datasets](https://www.comet.com/docs/opik/evaluation/manage_datasets) and compare aggregated [metrics](https://www.comet.com/docs/opik/evaluation/metrics/overview) to identify the best-performing [prompt](https://www.comet.com/docs/opik/prompt_engineering/prompt_management) strategy
* **Production Cost Considerations**: Configure sampling and filtering options for [online evaluation](https://www.comet.com/docs/opik/production/rules) to manage costs when running LLM-as-a-judge on production data
* **Scalable [Evaluation](https://www.comet.com/docs/opik/evaluation/overview) Approach**: Move beyond manual spot-checking to systematic assessment across 20+ test cases, enabling data-driven decisions for production deployment