LLM Evaluations

The provided image is a flowchart diagram titled “LLM Evaluations.” It visually describes the workflow for evaluating an AI Agent’s responses and the iterative process of tuning prompts.

Here is a step-by-step description of the diagram’s flow:

• Input Stage:
  • On the left side, there is a green block labeled “Question (Prompt).”
  • Below it, several supporting elements are listed: EVENT LOG, Config, Metrics, and Manual + @. These elements are grouped together within a light blue background, indicating they are all part of the prompt engineering or configuration process.
• Processing Stage:
  • An arrow points from the input section to the central purple block labeled “AI Agent,” which features a cute, smiling robot icon underneath it. This shows the prompt being fed into the AI system.
• Output & Evaluation Stage:
  • The AI Agent’s output travels via an arrow to a green block on the right labeled “Agent response Summary & Analysis.”
  • This response is directly compared (indicated by a black “VS” badge) against a grey block labeled “Answer Sheet.”
  • Attached to the “VS” badge is a blue circle that reads “By Another Agent.” This signifies that the comparative evaluation between the AI’s response and the correct answer sheet is performed automatically by a secondary AI agent.
• Scoring & Feedback Stage:
  • The result of the comparison flows down into a large, burgundy circle labeled “SCORE.”
  • At the bottom left, there is a light blue circle labeled “Tuning Prompt.” A dashed purple arrow connects this circle directly to the “SCORE” circle, accompanied by the text “To get more .” This illustrates a feedback loop where prompts are iteratively tuned and improved to achieve better evaluation scores.
• Additional Details:
  • In the top right corner, there is a small box containing a URL ([http://eeumee.net](http://eeumee.net)) and an email address (lechuck.park@gmail.com), likely indicating the creator or source of the diagram.

📝 Summary

This diagram illustrates the lifecycle of an automated LLM evaluation system. It shows how a prompt is processed by a primary AI agent, how the resulting response is evaluated against a golden answer sheet by a secondary AI agent to generate a score, and how that score drives the continuous tuning of the original prompt for better performance.This diagram illustrates the lifecycle of an automated LLM evaluation system. It shows how a prompt is processed by a primary AI agent, how the resulting response is evaluated against a golden answer sheet by a secondary AI agent to generate a score, and how that score drives the continuous tuning of the original prompt for better performance.

#LLM #AIAgent #PromptEngineering #LLMEvaluation #ArtificialIntelligence #PromptTuning #AIWorkflow