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merged 5 commits into from
Aug 7, 2025
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Prompt Migration Revisions for image and table rendering and links #2025

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dc6e6a1
fixing the links
corwin-oai Aug 7, 2025
a372332
rendering the raw markdown
corwin-oai Aug 7, 2025
c14d50e
moving cookbook into gpt-5 folder
corwin-oai Aug 7, 2025
10eacd5
fix image paths
corwin-oai Aug 7, 2025
6006e5d
notes formatting
corwin-oai Aug 7, 2025
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92 changes: 70 additions & 22 deletions examples/gpt-5/prompt-optimization-cookbook/prompt-optimization-cookbook.ipynb
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Expand Up @@ -15,9 +15,9 @@
"source": [
"The GPT-5 Family of models are the smartest models we’ve released to date, representing a step change in the models’ capabilities across the board. GPT-5 is particularly specialized in agentic task performance, coding, and steerability, making it a great fit for everyone from curious users to advanced researchers. \n",
"\n",
"GPT-5 will benefit from all the traditional prompting best practices, and to help you construct the best prompt we are introducing a [Prompting Guide for GPT-5](#) explaining how to make the most of its state-of-the-art capabilities. Alongside that, we are introducing a [GPT-5 Specific Prompt Optimizer](#https://platform.openai.com/chat/edit?optimize=true) in our Playground to help users get started on **improving existing prompts** and **migrating prompts** for GPT-5 and other OpenAI models.\n",
"GPT-5 will benefit from all the traditional prompting best practices, and to help you construct the best prompt we are introducing a [Prompting Guide for GPT-5](https://cookbook.openai.com/examples/gpt-5/gpt-5_prompting_guide) explaining how to make the most of its state-of-the-art capabilities. Alongside that, we are introducing a [GPT-5 Specific Prompt Optimizer](https://platform.openai.com/chat/edit?optimize=true) in our Playground to help users get started on **improving existing prompts** and **migrating prompts** for GPT-5 and other OpenAI models.\n",
"\n",
"In this cookbook we will go through how you can get spun up quickly to solve your task with GPT-5. We will share results of measurable improvements on common tasks and walk you through how you can use the Prompt Optimizer to do the same.\n"
"In this cookbook we will go through how you can get spun up quickly to solve your task with GPT-5. We will share results of measurable improvements on common tasks and walk you through how you can use the Prompt Optimizer to do the same."
]
},
{
Expand Down Expand Up @@ -548,6 +548,22 @@
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"### Prompt Optimization Results - Coding Tasks\n",
"\n",
"| Metric | Baseline | Optimized | Δ (Opt − Base) |\n",
"|----------------------------|---------:|----------:|---------------:|\n",
"| Avg Time (s) | 7.906 | 6.977 | -0.929 |\n",
"| Peak Memory (KB) | 3626.3 | 577.5 | -3048.8 |\n",
"| Exact (%) | 100.0 | 100.0 | 0.0 |\n",
"| Sorted (%) | 100.0 | 100.0 | 0.0 |\n",
"| LLM Adherence (1–5) | 4.40 | 4.90 | +0.50 |\n",
"| Code Quality (1–5) | 4.73 | 4.90 | +0.16 |\n"
]
}
],
"source": [
Expand All @@ -573,7 +589,9 @@
" judge_optimized=Path(\"results_llm_as_judge_optimized\")/\"judgement_summary.csv\",\n",
")\n",
"\n",
"display(Markdown(md))"
"display(Markdown(md))\n",
"\n",
"print(md)"
]
},
{
Expand Down Expand Up @@ -619,14 +637,6 @@
"We will run FailSafeQA evaluations via the helper script and compare Baseline vs Optimized prompts side by side."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c5849f77",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
Expand Down Expand Up @@ -834,7 +844,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 11,
"id": "c20097e6",
"metadata": {},
"outputs": [
Expand All @@ -845,10 +855,10 @@
"\n",
"**Compliance threshold:** ≥ 6\n",
"\n",
"| Metric | Baseline | Optimized | Δ (Opt − Base) |\n",
"|---|---:|---:|---:|\n",
"| Robustness (avg across datapoints) | 0.320 | 0.540 | +0.220 |\n",
"| Context Grounding (avg across datapoints) | 0.800 | 0.950 | +0.150 |\n",
"| Metric | Baseline | Optimized | Δ (Opt − Base) |\n",
"| ----------------------------------------- | -------- | --------- | -------------- |\n",
"| Robustness (avg across datapoints) | 0.320 | 0.540 | +0.220 |\n",
"| Context Grounding (avg across datapoints) | 0.800 | 0.950 | +0.150 |\n",
"\n",
"_Source files:_ `results_failsafeqa.csv` · `results_failsafeqa.csv`"
],
Expand All @@ -858,6 +868,22 @@
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"## FailSafeQA — Summary\n",
"\n",
"**Compliance threshold:** ≥ 6\n",
"\n",
"| Metric | Baseline | Optimized | Δ (Opt − Base) |\n",
"| ----------------------------------------- | -------- | --------- | -------------- |\n",
"| Robustness (avg across datapoints) | 0.320 | 0.540 | +0.220 |\n",
"| Context Grounding (avg across datapoints) | 0.800 | 0.950 | +0.150 |\n",
"\n",
"_Source files:_ `results_failsafeqa.csv` · `results_failsafeqa.csv`\n"
]
}
],
"source": [
Expand All @@ -872,20 +898,39 @@
") -> str:\n",
" d_r = robust_opt - robust_base\n",
" d_g = ground_opt - ground_base\n",
"\n",
" # Data rows\n",
" rows = [\n",
" [\"Metric\", \"Baseline\", \"Optimized\", \"Δ (Opt − Base)\"],\n",
" [\"Robustness (avg across datapoints)\", f\"{robust_base:.3f}\", f\"{robust_opt:.3f}\", f\"{d_r:+.3f}\"],\n",
" [\"Context Grounding (avg across datapoints)\", f\"{ground_base:.3f}\", f\"{ground_opt:.3f}\", f\"{d_g:+.3f}\"],\n",
" ]\n",
"\n",
" # Calculate column widths for alignment\n",
" col_widths = [max(len(str(row[i])) for row in rows) for i in range(len(rows[0]))]\n",
"\n",
" # Build table lines with padding\n",
" lines = []\n",
" for i, row in enumerate(rows):\n",
" padded = [str(cell).ljust(col_widths[j]) for j, cell in enumerate(row)]\n",
" lines.append(\"| \" + \" | \".join(padded) + \" |\")\n",
" if i == 0: # after header\n",
" sep = [\"-\" * col_widths[j] for j in range(len(row))]\n",
" lines.append(\"| \" + \" | \".join(sep) + \" |\")\n",
"\n",
" table = \"\\n\".join(lines)\n",
"\n",
" return f\"\"\"\n",
"## FailSafeQA — Summary\n",
"\n",
"**Compliance threshold:** ≥ {threshold}\n",
"\n",
"| Metric | Baseline | Optimized | Δ (Opt − Base) |\n",
"|---|---:|---:|---:|\n",
"| Robustness (avg across datapoints) | {robust_base:.3f} | {robust_opt:.3f} | {d_r:+.3f} |\n",
"| Context Grounding (avg across datapoints) | {ground_base:.3f} | {ground_opt:.3f} | {d_g:+.3f} |\n",
"{table}\n",
"\n",
"_Source files:_ `{src_base}` · `{src_opt}`\n",
"\"\"\".strip()\n",
"\n",
"# Fill in with your reported numbers\n",
"# Usage\n",
"md = build_markdown_summary_from_metrics(\n",
" robust_base=0.320, ground_base=0.800,\n",
" robust_opt=0.540, ground_opt=0.950,\n",
Expand All @@ -894,7 +939,10 @@
" src_opt=\"results_failsafeqa.csv\",\n",
")\n",
"\n",
"display(Markdown(md))"
"# Notebook pretty\n",
"display(Markdown(md))\n",
"\n",
"print(md)"
]
},
{
Expand Down