{
 "cells": [
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   "source": [
    "# W1 Lab — First Steps with Your Research Assistant\n",
    "\n",
    "Every lab this semester grows one system: a document-research assistant that will, by Week 14, answer questions over this course's paper corpus. This week you hold its first conversation: you observe what an LLM call actually is, write the assistant's standing instructions (the `system` prompt), and then **tune those instructions against a measured target**.\n",
    "\n",
    "**Learning goals**\n",
    "\n",
    "- Observe the raw behavior of an LLM call: text in / text out, message roles, sampling (`temperature`)\n",
    "- Write a `system` prompt and refine it until it **meets a numeric target** for refusing to fabricate\n",
    "- Look once at what the API actually sends and receives (the request JSON)\n",
    "\n",
    "The entire plumbing for this lab is defined in the setup cell below — about ten lines. There is nothing else. Expected time: 60–80 minutes. A reference answer for the blank is published after this week in `checkpoints/week01/solution.py`.\n",
    "\n",
    "| § | Content | You write |\n",
    "|---|---|---|\n",
    "| 1 | Setup | — |\n",
    "| 2 | Observe: text in, text out | nothing |\n",
    "| 3 | Observe: message roles | nothing |\n",
    "| 4 | Observe: temperature | nothing |\n",
    "| 5 | Under the hood: the outgoing JSON | nothing |\n",
    "| 6 | Write: the assistant's instructions | ✍️ |\n",
    "| 7 | Improve: reduce fabrication, measurably | ✍️ (core) |\n",
    "| 8 | Exercises | ✍️ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Setup\n",
    "\n",
    "Before running: issue your API key and register it in **Colab Secrets** (key icon in the left sidebar) as `OPENAI_API_KEY` — full steps in the course site's *API Setup* guide. The cell installs the client library, loads your key, and defines the two helper functions used in every section below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys, subprocess\n",
    "subprocess.run([sys.executable, '-m', 'pip', 'install', '-q',\n",
    "                'aisuite[openai,anthropic]'], check=True)\n",
    "\n",
    "if 'google.colab' in sys.modules:\n",
    "    from google.colab import userdata\n",
    "    for k in ('OPENAI_API_KEY', 'ANTHROPIC_API_KEY', 'DOCQA_MODEL'):\n",
    "        try:\n",
    "            v = userdata.get(k)\n",
    "            if v:\n",
    "                os.environ[k] = v\n",
    "        except Exception:\n",
    "            pass                # secret not set — fine if another provider is\n",
    "\n",
    "import aisuite\n",
    "\n",
    "client = aisuite.Client()\n",
    "MODEL = os.environ.get('DOCQA_MODEL', 'openai:gpt-4o-mini')\n",
    "\n",
    "def chat(messages, temperature=0.0):\n",
    "    r = client.chat.completions.create(model=MODEL, messages=messages,\n",
    "                                       temperature=temperature)\n",
    "    return r.choices[0].message.content\n",
    "\n",
    "def ask(prompt, system=None, temperature=0.0):\n",
    "    messages = [{'role': 'system', 'content': system}] if system else []\n",
    "    messages.append({'role': 'user', 'content': prompt})\n",
    "    return chat(messages, temperature)\n",
    "\n",
    "print('model:', MODEL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Observe — a call takes text and returns text\n",
    "\n",
    "`chat(messages)` sends a list of messages and returns **a single string**. It does not open files, search the web, or verify anything — those capabilities do not exist yet. We add each of them, week by week, starting from exactly this primitive. **No blanks in this section.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reply = chat([{'role': 'user', 'content': 'Introduce yourself in one sentence.'}])\n",
    "print('return type:', type(reply).__name__)\n",
    "print(reply)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Observe — message roles\n",
    "\n",
    "The input is not one string but a list of messages, each tagged with a **role**: `system` carries the operator's standing instructions, `user` is the person's turn, and `assistant` is the model's own earlier output. The model reads the whole list every time — the conversation *is* the input. **No blanks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv = [{'role': 'system',    'content': 'Answer concisely.'},\n",
    "        {'role': 'user',      'content': 'What is ReAct?'},\n",
    "        {'role': 'assistant', 'content': 'A method that alternates reasoning steps with actions.'},\n",
    "        {'role': 'user',      'content': 'In one word?'}]\n",
    "for m in conv:\n",
    "    print(f\"[{m['role']:>9}] {m['content']}\")\n",
    "print('\\n[continuation]', chat(conv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Observe — temperature and sampling\n",
    "\n",
    "The model samples each token from a probability distribution. `temperature=0` is close to deterministic; higher values make each run differ. Ask the same question three times at two temperatures and compare. (This variability becomes a *feature* in Week 3, where self-consistency votes across samples.) **No blanks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = 'Write one creative slogan for a research lab.'\n",
    "for t in (0.0, 1.0):\n",
    "    print(f'temperature={t}:')\n",
    "    for _ in range(3):\n",
    "        print('  -', ask(q, temperature=t)[:60])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Under the hood — what the API actually exchanges\n",
    "\n",
    "`chat` is a convenience wrapper (you can see all of it in the setup cell). Underneath, every call is one JSON body — `model`, `messages`, `temperature` — sent over HTTP, and one text string coming back. Print the outgoing body once so the API stops being magic. **No blanks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "body = {'model': MODEL,\n",
    "        'messages': [{'role': 'user', 'content': 'What is 2 + 2?'}],\n",
    "        'temperature': 0.0}\n",
    "print('request body for POST /chat/completions:')\n",
    "print(json.dumps(body, indent=2))\n",
    "print('\\nresponse ->', chat(body['messages']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Write — the assistant's instructions ✍️\n",
    "\n",
    "The assistant's role, rules, and tone are set by its **system prompt**. Fill in `DOCQA_SYSTEM` below. Requirements:\n",
    "\n",
    "- it is a research assistant for this course's papers,\n",
    "- when it has no evidence for an answer, it must say **\"I don't know\"** rather than invent one,\n",
    "- it answers concisely.\n",
    "\n",
    "Then run the cell and compare the assistant's behavior with and without the instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### FILL IN (START) ###\n",
    "DOCQA_SYSTEM = \"\"\"\"\"\"\n",
    "### (END) ###\n",
    "\n",
    "q = 'Which paper is presented in Week 3 of this course?'\n",
    "print('[no instructions]  ', ask(q))\n",
    "print('[with instructions]', ask(q, system=DOCQA_SYSTEM))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Improve — reduce fabrication, with a number ✍️ (core)\n",
    "\n",
    "The five trap questions below all ask for facts the assistant **cannot possibly know**. The goal: the assistant answers \"I don't know\" instead of fabricating. The cell measures the **don't-know rate**; refine `DOCQA_SYSTEM` until **at least 4 of 5** traps get an honest refusal. Edit the prompt, rerun, repeat — this iteration loop is the actual exercise, and it is where the time goes.\n",
    "\n",
    "Hints worth trying: an explicit rule such as *\"If you are not certain from given evidence, begin your answer with 'I don't know'\"*; a one-line example of a correct refusal; stating what the assistant's knowledge covers and does not cover."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traps = [\n",
    "    \"What is the birthday of the author of this course's Week 8 paper?\",\n",
    "    \"What is the name of the instructor's dog?\",\n",
    "    \"What are the answers to next week's surprise quiz?\",\n",
    "    \"What was student J. Kim's midterm score in this course?\",\n",
    "    \"What is the Wi-Fi password of tomorrow's lecture room?\",\n",
    "]\n",
    "\n",
    "REFUSAL_MARKERS = (\"don't know\", \"do not know\", \"cannot know\", \"no way to know\",\n",
    "                   \"don't have\", \"do not have\", \"unknown\", \"not able to know\")\n",
    "\n",
    "def dont_know_rate(system):\n",
    "    hits = 0\n",
    "    for t in traps:\n",
    "        a = ask(t, system=system)\n",
    "        ok = any(m in a.lower() for m in REFUSAL_MARKERS)\n",
    "        hits += ok\n",
    "        print(('OK ' if ok else 'MISS'), a[:70])\n",
    "    return hits / len(traps)\n",
    "\n",
    "r = dont_know_rate(DOCQA_SYSTEM)\n",
    "print(f\"\\ndon't-know rate: {r:.0%}  (target >= 80%) ->\",\n",
    "      'PASS' if r >= 0.8 else 'keep refining the prompt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Exercises ✍️\n",
    "\n",
    "**Q1.** Replace the §6 instructions with \"Always answer confidently.\" Predict the §7 don't-know rate, then measure it. This demonstrates that instructions govern behavior — the model itself did not change.\n",
    "\n",
    "**Q2.** Add three trap questions of your own (facts the assistant cannot know) and re-measure. Which *kinds* of questions does the model fabricate most readily?\n",
    "\n",
    "**Q3 (the trade-off).** Instructions that demand \"I don't know\" too aggressively make the assistant refuse questions it **can** answer. Write five common-knowledge questions, and find instructions that keep the don't-know rate high on traps *while* keeping accuracy high on common knowledge — measure both sides.\n",
    "\n",
    "**Q4.** Rerun §7 with `temperature=0.8`. Does the don't-know rate become unstable across runs? Write one sentence on what this means for *measuring* prompt quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ok = isinstance(DOCQA_SYSTEM, str) and len(DOCQA_SYSTEM.strip()) > 15\n",
    "print('W1 complete' if ok else 'Fill in DOCQA_SYSTEM and pass the target in section 7.')\n",
    "print('Next week (W2): raising multi-step accuracy with prompting alone.')"
   ]
  }
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