prompts/stanford/2_generate_course_outlines.ipynb

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   "source": [
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"stanford_courses_cleaned_non_generic.csv\", dtype=str)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "1-shot generation of course outlines from the title and description"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "255265fa0caac0da"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from string import Template\n",
    "\n",
    "OUTLINE_TEMPLATE = Template(\"\"\"Write a course outline for a textbook on \\\"The Global Positioning System: Where on Earth are We, and What Time is It?\\\" covering the following topics: \\\"Why people want to know where they are: answers include cross-Pacific trips of Polynesians, missile guidance, and distraught callers. How people determine where they are: navigation technology from dead-reckoning, sextants, and satellite navigation (GPS). Hands-on experience. How GPS works; when it does not work; possibilities for improving performance.\\\".\n",
    "Model: 1. Introduction\n",
    "- What is the Global Positioning System?\n",
    "- Importance of GPS\n",
    "- Overview of the course\n",
    "\n",
    "2. Navigation technology\n",
    "- Dead-reckoning\n",
    "- Sextants\n",
    "- Satellite navigation\n",
    "- Comparison of technologies\n",
    "- Hands-on experience with navigation technology\n",
    "\n",
    "3. GPS technology\n",
    "- How GPS works\n",
    "    - Satellites\n",
    "    - Ground receivers\n",
    "    - Triangulation\n",
    "- When GPS does not work\n",
    "    - Blockage\n",
    "    - Multipath\n",
    "- Possibilities for improving performance\n",
    "\n",
    "4. Applications of GPS\n",
    "- Cross-Pacific trips of Polynesians\n",
    "- Missile guidance\n",
    "- Distraught callers\n",
    "- Other applications of GPS\n",
    "\n",
    "User: Write a course outline for a textbook on \\\"${COURSE_TITLE}\\\" covering the following topics: \\\"${COURSE_DESCRIPTION}\\\". Do not include assignments, exams or prerequisites.\n",
    "Model: \"\"\")"
   ],
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   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "courses_to_generate = []\n",
    "for a, b in df.iterrows():\n",
    "    prompt = OUTLINE_TEMPLATE.substitute({\"COURSE_TITLE\": b[\"title\"], \"COURSE_DESCRIPTION\": b[\"description\"]})\n",
    "    courses_to_generate.append({\n",
    "        \"course_title\": b[\"title\"],\n",
    "        \"course_description\": b[\"description\"],\n",
    "        \"prompt\": prompt,\n",
    "    })"
   ],
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   "id": "de59641276702b38"
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  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "generations = [...]  # code to generate using the prompts here"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "29965888236f2bdd"
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  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "for course, generation in zip(courses_to_generate, generations):\n",
    "    course[\"outline\"] = generation\n",
    "\n",
    "pd.DataFrame(courses_to_generate).to_csv(\"outlines_full.csv\")"
   ],
   "metadata": {
    "collapsed": false
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   "id": "55c5350ab00b5d54"
  },
  {
   "cell_type": "markdown",
   "source": [
    "(very large) 2-shot prompt to have the model correct and clean up the generated outlines"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c0d4dd91d694584f"
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  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "OUTLINE_FILTER_TEMPLATE = Template(\"\"\"The following is a course outline for a course on \\\"Anesthesia Operating Room Clerkship\\\". This outline needs to be anonymized and adapted to an online audience:\n",
    "1.1 Introduction: Overview of the Anesthesia Operating Room Clerkship\n",
    "1.2 Introduction: Objectives of the clerkship\n",
    "1.3 Introduction: Prerequisites for the clerkship\n",
    "2.1 Clinical settings: Sequoia Hospital in Redwood City\n",
    "2.2 Clinical settings: Outpatient surgery centers throughout the community\n",
    "2.3 Clinical settings: Exposure to general and regional anesthetic techniques\n",
    "2.4 Clinical settings: Adult and pediatric patients\n",
    "3.1 Personalized discussion: Applied physiology\n",
    "3.2 Personalized discussion: Pharmacology\n",
    "3.3 Personalized discussion: Pathophysiology of the surgical patient\n",
    "3.4 Personalized discussion: Daily basis\n",
    "3.5 Personalized discussion: Final paper to be submitted by the students\n",
    "4.1 Transportation: Students need to arrange transportation to the various workplaces\n",
    "5.1 Prerequisites: A major clerkship in medicine or surgery is strongly recommended\n",
    "6.1 Periods available: 1-12, full-time for 2 weeks\n",
    "6.2 Periods available: 1 student per period\n",
    "7.1 Clerkship director and coordinator: Kurt Fink, M.D.\n",
    "7.2 Clerkship director and coordinator: Yun Tao, 650-724-1706, yuntao@stanford.edu, Stanford Hospital\n",
    "8.1 Reporting instructions: Contact Dr. Kurt Fink one week prior\n",
    "8.2 Reporting instructions: Time: TBA\n",
    "8.3 Reporting instructions: Call code: 0\n",
    "9.1 Other faculty: Palo Alto Medical Clinic Anesthesiologist\n",
    "10.1 Location: Palo Alto Medical Foundation.\n",
    "\n",
    "Which of the sections of the outline contain: \n",
    "- private faculty members information (names or contact information)\n",
    "- prerequisites, requirements, application processes or other practical course information not related to the course content\n",
    "- assignments, final papers, exams, presentations or other student evaluation information\n",
    "Falcon:\n",
    "- private faculty members information (names or contact information): 7.1, 7.2., 8.1, 9.1\n",
    "- prerequisites, requirements, application processes, schedules or other practical course information not related to the course content: 1.3, 4.1, 5.1, 6.1, 6.2, 8.1, 8.2, 8.3, 10.1\n",
    "- assignments, final papers, exams, presentations or other student evaluation information: 3.5\n",
    "User: The following is a course outline for a course on \"Numerical Methods for Compressible Flows\". This outline needs to be anonymized and adapted to an online audience:\n",
    "1.1 Introduction: Overview of the course\n",
    "1.2 Introduction: Importance of numerical methods for compressible flows\n",
    "1.3 Introduction: Prerequisites for the course\n",
    "2.1 Mathematical models for compressible flows: Hierarchy of mathematical models\n",
    "2.2 Mathematical models for compressible flows: Ideal potential flow\n",
    "2.3 Mathematical models for compressible flows: Transonic potential flow\n",
    "3.1 Numerical methods for compressible flows: Finite difference methods\n",
    "3.2 Numerical methods for compressible flows: Finite volume methods\n",
    "3.3 Numerical methods for compressible flows: Finite element methods\n",
    "4.1 Representative model problems: Shocks\n",
    "4.2 Representative model problems: Expansions\n",
    "5.1 Treatment of boundary conditions: Dirichlet boundary conditions\n",
    "5.2 Treatment of boundary conditions: Neumann boundary conditions\n",
    "6.1 Applications of numerical methods for compressible flows: Aerospace engineering\n",
    "6.3 Applications of numerical methods for compressible flows: Other applications of numerical methods for compressible flows\n",
    "\n",
    "Which of the sections of the outline contain: \n",
    "- private faculty members information (names or contact information)\n",
    "- prerequisites, requirements, application processes or other practical course information not related to the course content\n",
    "- assignments, final papers, exams, presentations or other student evaluation information\n",
    "Falcon: \n",
    "- private faculty members information (names or contact information): None\n",
    "- prerequisites, requirements, application processes, schedules or other practical course information not related to the course content: 1.3\n",
    "- assignments, final papers, exams, presentations or other student evaluation information: None\n",
    "User: The following is a course outline for a course on \\\"${COURSE_TITLE}\\\". This outline needs to be anonymized and adapted to an online audience:\n",
    "${SECTIONS_LIST}\n",
    "\n",
    "Which of the sections of the outline contain: \n",
    "- private faculty members information (names or contact information)\n",
    "- prerequisites, requirements, application processes, schedules or other practical course information not related to the course content\n",
    "- assignments, final papers, exams, presentations or other student evaluation information\n",
    "Falcon: \"\"\")"
   ],
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   "id": "53702cd87a677315"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Reformat cells into numbered format"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ca753f9d85a95139"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "FIND_SECTIONS_REGEX = re.compile(r\"\\d\\. .*(?:\\n\\s*- .*)+\")\n",
    "FIND_TITLES_REGEX = re.compile(r\"\\d\\. (.*)\")\n",
    "FIND_UNIT_TITLES_REGEX = re.compile(r\"\\n\\s*- (.*)\")\n",
    "\n",
    "def extract_sections(outline):\n",
    "    sections = FIND_SECTIONS_REGEX.findall(outline)\n",
    "    return [\n",
    "        {\n",
    "            \"section_nr\": si + 1,\n",
    "            \"title\": FIND_TITLES_REGEX.search(section).group(1),\n",
    "            \"unit_titles\": FIND_UNIT_TITLES_REGEX.findall(section),\n",
    "        } for si, section in enumerate(sections)\n",
    "    ]\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"outlines_full.csv\", dtype=str)\n",
    "for a, b in df.iterrows():\n",
    "    sections = extract_sections(b[\"outline\"])\n",
    "    sections_list = '\\n'.join(\n",
    "        [f\"{si + 1}.{ui + 1} {section['title']}: {unit_title}\" for si, section in enumerate(sections) for\n",
    "         ui, unit_title in enumerate(section[\"unit_titles\"])])\n",
    "    prompt = OUTLINE_FILTER_TEMPLATE.substitute({\"COURSE_TITLE\": b[\"course_title\"], \"SECTIONS_LIST\": sections_list})\n",
    "    df.loc[a, 'filter_outline_prompt'] = prompt\n",
    "    df.loc[a, 'filter_outline_result'] = generate... # actually generate the filter results"
   ],
   "metadata": {
    "collapsed": false
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   "id": "1cfa8766032e5ee2"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df.to_csv(\"outlines_full_filtered.csv\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ea6a4080d3619d40"
  }
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