Concept

Experimentation is an evaluation-driven development approach to systematically select best prompt configuration and achieve consistent performance. It enables users to rapidly test, validate, and compare different prompt configurations against various evaluation criteria within a structured framework.

A prompt configuration here includes not only the input text but also the model configuration and other parameters that influence the model’s behaviour and output.

Evaluation criteria encompass various aspects of model output, such as context adherence, factual accuracy, and prompt perplexity. They guide the evaluation process by providing clear, measurable objectives that help identify areas for improvement and optimization.

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Why We Need Experimentation?

• Accelerated Iterations and Evaluation: It enables rapid prototyping and performance tuning by providing structured evaluations. This helps in quickly identifying effective strategies and configurations, such as temperature settings and token limits, allowing teams to efficiently iterate on model improvements.
• Comprehensive Performance Comparison: It offers dashboard that consolidate results, providing a single view to objectively assess which configurations or models perform best under specific conditions.
• Objective Decision-Making: By quantifying changes in model performance across various configurations and datasets, experimentation provides a data-driven approach to make informed decisions based on empirical evidence rather than intuition.
• Version Control and Historical Analysis: Storing all versions of experiments allows teams to track progress over time, analyze trends, understand model behaviour, and ensure consistent and meaningful improvements.

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Working of Experimentation

[better if a diagram of below process]

The key steps in experimentation include:

1. Defining the Experiment Scope:
   • Selecting a dataset that represents inputs and expected outputs.
   • Identifying the prompt structure, or model configurations to be tested.
   • Establishing evaluation metrics to assess the effectiveness of different configurations.
2. Executing the Experiment:
   • Running multiple test cases using the defined configurations.
   • Capturing LLM-generated responses for each variation.
3. Evaluating Model Performance:
   • Applying automated evaluators to score responses based on accuracy, fluency, coherence, and factual correctness.
   • Running LLM-based assessments, rule-based checks, or human reviews for deeper analysis.
4. Comparing Results & Identifying Optimal Configurations:
   • Comparing different prompt versions or model outputs side by side.
   • Measuring improvements based on evaluation scores.
   • Determining which configuration performs best across different datasets and scenarios.
5. Iterating & Deploying Changes:
   • Using insights from experimentation to optimise LLM pipelines.
   • Refining prompts, retrieval strategies, or model parameters for improved consistency.
   • Repeating the process in a continuous feedback loop to ensure long-term AI performance improvements.

By following this systematic approach, experimentation transforms AI development from a trial-and-error process into a structured, data-driven workflow, allowing teams to make informed decisions and scale AI applications with confidence.