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Overview

This lesson teaches learners how to systematically assess LLM outputs, identify errors, debug issues, and iteratively improve prompts and workflows. Evaluation is critical for producing consistent, accurate, and trustworthy AI outputs.

Concept Explanation

1. Why Evaluation Matters

• LLMs are probabilistic; outputs can vary even with the same prompt.
• Evaluation ensures:
  • Accuracy: Correctness of facts and reasoning.
  • Relevance: Alignment with user needs.
  • Consistency: Repeatable and reliable outputs.
  • Bias Detection: Avoid unwanted or harmful outputs.

2. Levels of Evaluation

a) Prompt-Level Evaluation

• Check if a specific prompt produces the desired output.
• Strategies:
  • Test with multiple inputs.
  • Compare outputs against expected results.
  • Refine wording, examples, and instructions iteratively.

b) Workflow-Level Evaluation

• Examine end-to-end application outputs.
• Evaluate each step:
  • Input transformation
  • LLM reasoning
  • Output post-processing
• Helps isolate which step introduces errors.

c) Quantitative Metrics

• Accuracy / Precision / Recall: For classification tasks.
• ROUGE / BLEU / METEOR: For summarization or translation tasks.
• Logprob scores: Identify uncertain or low-confidence predictions.

d) Human Evaluation

• Essential for:
  • Subjective tasks (creative writing, summarization, advice).
  • Evaluating tone, readability, and appropriateness.

3. Debugging LLM Outputs

• Common issues:
  • Hallucinations (fabricated facts)
  • Repetition or verbose outputs
  • Misclassification or off-topic responses
• Debugging steps:
  1. Examine prompt structure.
  2. Adjust context or role instructions.
  3. Modify sampling settings (temperature, top-K, top-P).
  4. Test with few-shot examples.
  5. Iterate and log results.

4. Iterative Improvement

• Track outputs and changes for each iteration.
• Use feedback loops to refine both prompts and workflow design.
• Apply self-consistency or multiple completions for high-confidence outputs.
• Use automated tests for regression checks in production.

Practical Examples / Techniques

1. Debugging a Summary Prompt

Prompt: "Summarize this article in 3 sentences."
Problem: Output is too verbose.
Solution: Add constraint: "Summarize in exactly 3 concise sentences, no extra commentary."

2. Reducing Hallucinations

Prompt: "List 5 top AI startups founded in 2024." 
Fix: Include source context or retrieval step: "Using the following verified list of AI startups, list the top 5..."

3. Self-Consistency Check

• Generate 5 outputs for a reasoning task.
• Compare answers; select majority or consensus for reliability.

Hands-on Project / Exercise

Task: Improve reliability of an LLM workflow for customer support.

Steps:

1. Draft an initial workflow for classifying and responding to tickets.
2. Generate outputs for 20 sample tickets.
3. Identify errors or inconsistencies.
4. Refine prompts, add examples, or adjust LLM settings.
5. Repeat until outputs are consistent and accurate.

Goal: Deliver a workflow with ≥90% reliable classification and response quality.

Tools & Techniques

• APIs: OpenAI GPT, Vertex AI, Claude for testing outputs.
• Evaluation libraries: BLEU, ROUGE, or custom scoring scripts.
• Logging frameworks: Track prompt versions, settings, outputs.
• Feedback loops: Use outputs to iteratively refine prompts and workflow.

Audience Relevance

• Developers: Build robust, production-ready AI applications.
• Students & Researchers: Learn systematic evaluation and debugging methods.
• Business Users: Ensure AI outputs meet company standards for accuracy and relevance.

Summary & Key Takeaways

• Evaluation occurs at prompt and workflow levels.
• Debugging involves prompt adjustments, context refinement, and sampling control.
• Iterative improvement ensures high-quality, consistent, and reliable LLM outputs.
• Logging and metrics are essential for scalable and maintainable applications.
• Combining human and automated evaluation produces trustworthy AI solutions.