Prompt workflows for model performance notes

Here’s a practical workflow for prompting and documenting model performance notes, particularly in scenarios involving language models like ChatGPT, fine-tuned LLMs, or similar AI systems. This is especially useful for QA teams, researchers, developers, or prompt engineers who aim to monitor, evaluate, and iterate on model behavior over time.

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1. Define the Evaluation Scope

• Use case: What’s the task? (e.g., summarization, sentiment analysis, classification, content generation)

• Metric goals: What defines “good” performance? (e.g., accuracy, fluency, bias, factuality)

• Constraints: Word limits, tone, style, safety filters, etc.

📌 Example Note:
Task: Legal summarization.
Goal: Extract core legal implications from long-form documents using concise, plain language.
Constraints: Max 300 words, no legal jargon.

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2. Set Up Prompt Templates

Use standardized prompt templates to test behavior consistently.

• Instructional Prompts: Direct instructions.

• Few-shot Prompts: Include examples.

• Chain-of-thought Prompts: Encourage step-by-step reasoning.

• Role-based Prompts: Assign an identity or expertise level.

📌 Example Prompt:
“You are a financial analyst. Summarize the key risks and opportunities from the following earnings call transcript…”

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3. Record Prompt Variants

Track changes to wording, structure, and formatting to observe how they affect outputs.

• Version 1: “Summarize this text.”

• Version 2: “In 3 bullet points, summarize the main idea, key findings, and implications.”

• Version 3: “Explain this report like I’m a 10-year-old.”

📌 Model Note:
Prompt v2 yielded clearer structure and better focus. Prompt v3 simplified too much and lost nuance.

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4. Collect and Annotate Outputs

For each prompt variant:

• Log raw output

• Tag errors, strengths, anomalies

• Rate quality (e.g., 1–5 scale)

📌 Annotation Example:
Output: “The study shows that X is better than Y…”
Note: Lacks citation, overconfident tone. Quality rating: 3/5.

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5. Compare Against Baselines

Use:

• Ground truth references (human-written answers)

• Previous model versions

• Other LLMs (GPT-4 vs Claude vs Llama)

📌 Comparison Note:
GPT-4 was more verbose but covered all points. Claude missed key insight. Human reference had more structured layout.

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6. Identify Failure Modes

Track recurring failure types:

• Hallucinations

• Repetitions

• Incomplete responses

• Style violations

• Safety/toxicity flags

📌 Failure Example:
Prompt: “Write a motivational letter…”
Failure: Included fictional degrees and achievements.
Action: Add constraint: “Use only information provided.”

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7. Document Improvement Ideas

After evaluations, note prompt revisions or model fine-tuning suggestions.

• Add clarifying constraints.

• Reword vague instructions.

• Provide examples to guide behavior.

📌 Improvement Note:
Add few-shot examples to help model learn summary structure. Add “Do not repeat source text” instruction.

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8. Use a Standardized Note Template

Use this structure for each entry:

yaml
CopyEdit
---  
**Prompt ID:** PRT-001  
**Prompt Version:** v1.2  
**Task:** Product Review Summary  
**Model:** GPT-4-Turbo  
**Output Quality:** 4/5  
**Strengths:** Concise, accurate tone  
**Issues:** Slight redundancy, missed minor detail  
**Failures:** None  
**Suggestions:** Reduce repetition via explicit instruction  
**Next Steps:** Test v1.3 with revised tone and structure  
---

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9. Centralize Performance Logs

Use tools like:

• Notion or Airtable for structured entries

• Git (for prompt versioning)

• Spreadsheets for scoring

• Jupyter or dashboards for analytics (if automated testing is involved)

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10. Iterate, Retest, and Track Trends

• Group notes by task type

• Visualize performance trends (scores, failure rates)

• Link prompt improvements to outcome changes

📊 Insight: Prompts with chain-of-thought guidance reduced hallucinations by 20%.

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This workflow helps in building a repeatable, data-informed prompt optimization process that supports better model alignment and user satisfaction.