recipes/quickstart/Prompt_Engineering_with_Llama_3.ipynb

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    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/Prompt_Engineering_with_Llama_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "# Prompt Engineering with Llama 3.1\n",
    "\n",
    "Prompt engineering is using natural language to produce a desired response from a large language model (LLM).\n",
    "\n",
    "This interactive guide covers prompt engineering & best practices with Llama 3.1."
   ]
  },
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   "source": [
    "## Introduction"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "### Why now?\n",
    "\n",
    "[Vaswani et al. (2017)](https://arxiv.org/abs/1706.03762) introduced the world to transformer neural networks (originally for machine translation). Transformers ushered an era of generative AI with diffusion models for image creation and large language models (`LLMs`) as **programmable deep learning networks**.\n",
    "\n",
    "Programming foundational LLMs is done with natural language – it doesn't require training/tuning like ML models of the past. This has opened the door to a massive amount of innovation and a paradigm shift in how technology can be deployed. The science/art of using natural language to program language models to accomplish a task is referred to as **Prompt Engineering**."
   ]
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   "source": [
    "### Llama Models\n",
    "\n",
    "In 2023, Meta introduced the [Llama language models](https://ai.meta.com/llama/) (Llama Chat, Code Llama, Llama Guard). These are general purpose, state-of-the-art LLMs.\n",
    "\n",
    "Llama models come in varying parameter sizes. The smaller models are cheaper to deploy and run; the larger models are more capable.\n",
    "\n",
    "#### Llama 3.1\n",
    "1. `llama-3.1-8b` - base pretrained 8 billion parameter model\n",
    "1. `llama-3.1-70b` - base pretrained 70 billion parameter model\n",
    "1. `llama-3.1-405b` - base pretrained 405 billion parameter model\n",
    "1. `llama-3.1-8b-instruct` - instruction fine-tuned 8 billion parameter model\n",
    "1. `llama-3.1-70b-instruct` - instruction fine-tuned 70 billion parameter model\n",
    "1. `llama-3.1-405b-instruct` - instruction fine-tuned 405 billion parameter model (flagship)\n",
    "\n",
    "\n",
    "#### Llama 3\n",
    "1. `llama-3-8b` - base pretrained 8 billion parameter model\n",
    "1. `llama-3-70b` - base pretrained 70 billion parameter model\n",
    "1. `llama-3-8b-instruct` - instruction fine-tuned 8 billion parameter model\n",
    "1. `llama-3-70b-instruct` - instruction fine-tuned 70 billion parameter model (flagship)\n",
    "\n",
    "#### Llama 2\n",
    "1. `llama-2-7b` - base pretrained 7 billion parameter model\n",
    "1. `llama-2-13b` - base pretrained 13 billion parameter model\n",
    "1. `llama-2-70b` - base pretrained 70 billion parameter model\n",
    "1. `llama-2-7b-chat` - chat fine-tuned 7 billion parameter model\n",
    "1. `llama-2-13b-chat` - chat fine-tuned 13 billion parameter model\n",
    "1. `llama-2-70b-chat` - chat fine-tuned 70 billion parameter model (flagship)\n"
   ]
  },
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   "source": [
    "Code Llama is a code-focused LLM built on top of Llama 2 also available in various sizes and finetunes:"
   ]
  },
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   "source": [
    "#### Code Llama\n",
    "1. `codellama-7b` - code fine-tuned 7 billion parameter model\n",
    "1. `codellama-13b` - code fine-tuned 13 billion parameter model\n",
    "1. `codellama-34b` - code fine-tuned 34 billion parameter model\n",
    "1. `codellama-70b` - code fine-tuned 70 billion parameter model\n",
    "1. `codellama-7b-instruct` - code & instruct fine-tuned 7 billion parameter model\n",
    "2. `codellama-13b-instruct` - code & instruct fine-tuned 13 billion parameter model\n",
    "3. `codellama-34b-instruct` - code & instruct fine-tuned 34 billion parameter model\n",
    "3. `codellama-70b-instruct` - code & instruct fine-tuned 70 billion parameter model\n",
    "1. `codellama-7b-python` - Python fine-tuned 7 billion parameter model\n",
    "2. `codellama-13b-python` - Python fine-tuned 13 billion parameter model\n",
    "3. `codellama-34b-python` - Python fine-tuned 34 billion parameter model\n",
    "3. `codellama-70b-python` - Python fine-tuned 70 billion parameter model"
   ]
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   "source": [
    "## Getting an LLM\n",
    "\n",
    "Large language models are deployed and accessed in a variety of ways, including:\n",
    "\n",
    "1. **Self-hosting**: Using local hardware to run inference. Ex. running Llama on your Macbook Pro using [llama.cpp](https://github.com/ggerganov/llama.cpp).\n",
    "    * Best for privacy/security or if you already have a GPU.\n",
    "1. **Cloud hosting**: Using a cloud provider to deploy an instance that hosts a specific model. Ex. running Llama on cloud providers like AWS, Azure, GCP, and others.\n",
    "    * Best for customizing models and their runtime (ex. fine-tuning a model for your use case).\n",
    "1. **Hosted API**: Call LLMs directly via an API. There are many companies that provide Llama inference APIs including AWS Bedrock, Replicate, Anyscale, Together and others.\n",
    "    * Easiest option overall."
   ]
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    "### Hosted APIs\n",
    "\n",
    "Hosted APIs are the easiest way to get started. We'll use them here. There are usually two main endpoints:\n",
    "\n",
    "1. **`completion`**: generate a response to a given prompt (a string).\n",
    "1. **`chat_completion`**: generate the next message in a list of messages, enabling more explicit instruction and context for use cases like chatbots."
   ]
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   "source": [
    "## Tokens\n",
    "\n",
    "LLMs process inputs and outputs in chunks called *tokens*. Think of these, roughly, as words – each model will have its own tokenization scheme. For example, this sentence...\n",
    "\n",
    "> Our destiny is written in the stars.\n",
    "\n",
    "...is tokenized into `[\"Our\", \" destiny\", \" is\", \" written\", \" in\", \" the\", \" stars\", \".\"]` for Llama 3. See [this](https://tiktokenizer.vercel.app/?model=meta-llama%2FMeta-Llama-3-8B) for an interactive tokenizer tool.\n",
    "\n",
    "Tokens matter most when you consider API pricing and internal behavior (ex. hyperparameters).\n",
    "\n",
    "Each model has a maximum context length that your prompt cannot exceed. That's 128k tokens for Llama 3.1, 4K for Llama 2, and 100K for Code Llama.\n"
   ]
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   "source": [
    "## Notebook Setup\n",
    "\n",
    "The following APIs will be used to call LLMs throughout the guide. As an example, we'll call Llama 3.1 chat using [Grok](https://console.groq.com/playground?model=llama3-70b-8192).\n",
    "\n",
    "To install prerequisites run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install groq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from typing import Dict, List\n",
    "from groq import Groq\n",
    "\n",
    "# Get a free API key from https://console.groq.com/keys\n",
    "os.environ[\"GROQ_API_KEY\"] = \"YOUR_GROQ_API_KEY\"\n",
    "\n",
    "LLAMA3_405B_INSTRUCT = \"llama-3.1-405b-reasoning\" # Note: Groq currently only gives access here to paying customers for 405B model\n",
    "LLAMA3_70B_INSTRUCT = \"llama-3.1-70b-versatile\"\n",
    "LLAMA3_8B_INSTRUCT = \"llama3.1-8b-instant\"\n",
    "\n",
    "DEFAULT_MODEL = LLAMA3_70B_INSTRUCT\n",
    "\n",
    "client = Groq()\n",
    "\n",
    "def assistant(content: str):\n",
    "    return { \"role\": \"assistant\", \"content\": content }\n",
    "\n",
    "def user(content: str):\n",
    "    return { \"role\": \"user\", \"content\": content }\n",
    "\n",
    "def chat_completion(\n",
    "    messages: List[Dict],\n",
    "    model = DEFAULT_MODEL,\n",
    "    temperature: float = 0.6,\n",
    "    top_p: float = 0.9,\n",
    ") -> str:\n",
    "    response = client.chat.completions.create(\n",
    "        messages=messages,\n",
    "        model=model,\n",
    "        temperature=temperature,\n",
    "        top_p=top_p,\n",
    "    )\n",
    "    return response.choices[0].message.content\n",
    "        \n",
    "\n",
    "def completion(\n",
    "    prompt: str,\n",
    "    model: str = DEFAULT_MODEL,\n",
    "    temperature: float = 0.6,\n",
    "    top_p: float = 0.9,\n",
    ") -> str:\n",
    "    return chat_completion(\n",
    "        [user(prompt)],\n",
    "        model=model,\n",
    "        temperature=temperature,\n",
    "        top_p=top_p,\n",
    "    )\n",
    "\n",
    "def complete_and_print(prompt: str, model: str = DEFAULT_MODEL):\n",
    "    print(f'==============\\n{prompt}\\n==============')\n",
    "    response = completion(prompt, model)\n",
    "    print(response, end='\\n\\n')\n"
   ]
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   "source": [
    "### Completion APIs\n",
    "\n",
    "Let's try Llama 3.1!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\"The typical color of the sky is: \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\"which model version are you?\")"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### Chat Completion APIs\n",
    "Chat completion models provide additional structure to interacting with an LLM. An array of structured message objects is sent to the LLM instead of a single piece of text. This message list provides the LLM with some \"context\" or \"history\" from which to continue.\n",
    "\n",
    "Typically, each message contains `role` and `content`:\n",
    "* Messages with the `system` role are used to provide core instruction to the LLM by developers.\n",
    "* Messages with the `user` role are typically human-provided messages.\n",
    "* Messages with the `assistant` role are typically generated by the LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = chat_completion(messages=[\n",
    "    user(\"My favorite color is blue.\"),\n",
    "    assistant(\"That's great to hear!\"),\n",
    "    user(\"What is my favorite color?\"),\n",
    "])\n",
    "print(response)\n",
    "# \"Sure, I can help you with that! Your favorite color is blue.\""
   ]
  },
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   "source": [
    "### LLM Hyperparameters\n",
    "\n",
    "#### `temperature` & `top_p`\n",
    "\n",
    "These APIs also take parameters which influence the creativity and determinism of your output.\n",
    "\n",
    "At each step, LLMs generate a list of most likely tokens and their respective probabilities. The least likely tokens are \"cut\" from the list (based on `top_p`), and then a token is randomly selected from the remaining candidates (`temperature`).\n",
    "\n",
    "In other words: `top_p` controls the breadth of vocabulary in a generation and `temperature` controls the randomness within that vocabulary. A temperature of ~0 produces *almost* deterministic results.\n",
    "\n",
    "[Read more about temperature setting here](https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683).\n",
    "\n",
    "Let's try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_tuned_completion(temperature: float, top_p: float):\n",
    "    response = completion(\"Write a haiku about llamas\", temperature=temperature, top_p=top_p)\n",
    "    print(f'[temperature: {temperature} | top_p: {top_p}]\\n{response.strip()}\\n')\n",
    "\n",
    "print_tuned_completion(0.01, 0.01)\n",
    "print_tuned_completion(0.01, 0.01)\n",
    "# These two generations are highly likely to be the same\n",
    "\n",
    "print_tuned_completion(1.0, 1.0)\n",
    "print_tuned_completion(1.0, 1.0)\n",
    "# These two generations are highly likely to be different"
   ]
  },
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   "source": [
    "## Prompting Techniques"
   ]
  },
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   "source": [
    "### Explicit Instructions\n",
    "\n",
    "Detailed, explicit instructions produce better results than open-ended prompts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(prompt=\"Describe quantum physics in one short sentence of no more than 12 words\")\n",
    "# Returns a succinct explanation of quantum physics that mentions particles and states existing simultaneously."
   ]
  },
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   "source": [
    "You can think about giving explicit instructions as using rules and restrictions to how Llama 3 responds to your prompt.\n",
    "\n",
    "- Stylization\n",
    "    - `Explain this to me like a topic on a children's educational network show teaching elementary students.`\n",
    "    - `I'm a software engineer using large language models for summarization. Summarize the following text in under 250 words:`\n",
    "    - `Give your answer like an old timey private investigator hunting down a case step by step.`\n",
    "- Formatting\n",
    "    - `Use bullet points.`\n",
    "    - `Return as a JSON object.`\n",
    "    - `Use less technical terms and help me apply it in my work in communications.`\n",
    "- Restrictions\n",
    "    - `Only use academic papers.`\n",
    "    - `Never give sources older than 2020.`\n",
    "    - `If you don't know the answer, say that you don't know.`\n",
    "\n",
    "Here's an example of giving explicit instructions to give more specific results by limiting the responses to recently created sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\"Explain the latest advances in large language models to me.\")\n",
    "# More likely to cite sources from 2017\n",
    "\n",
    "complete_and_print(\"Explain the latest advances in large language models to me. Always cite your sources. Never cite sources older than 2020.\")\n",
    "# Gives more specific advances and only cites sources from 2020"
   ]
  },
  {
   "attachments": {},
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   "source": [
    "### Example Prompting using Zero- and Few-Shot Learning\n",
    "\n",
    "A shot is an example or demonstration of what type of prompt and response you expect from a large language model. This term originates from training computer vision models on photographs, where one shot was one example or instance that the model used to classify an image ([Fei-Fei et al. (2006)](http://vision.stanford.edu/documents/Fei-FeiFergusPerona2006.pdf)).\n",
    "\n",
    "#### Zero-Shot Prompting\n",
    "\n",
    "Large language models like Llama 3 are unique because they are capable of following instructions and producing responses without having previously seen an example of a task. Prompting without examples is called \"zero-shot prompting\".\n",
    "\n",
    "Let's try using Llama 3 as a sentiment detector. You may notice that output format varies - we can improve this with better prompting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\"Text: This was the best movie I've ever seen! \\n The sentiment of the text is: \")\n",
    "# Returns positive sentiment\n",
    "\n",
    "complete_and_print(\"Text: The director was trying too hard. \\n The sentiment of the text is: \")\n",
    "# Returns negative sentiment"
   ]
  },
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   "source": [
    "\n",
    "#### Few-Shot Prompting\n",
    "\n",
    "Adding specific examples of your desired output generally results in more accurate, consistent output. This technique is called \"few-shot prompting\".\n",
    "\n",
    "In this example, the generated response follows our desired format that offers a more nuanced sentiment classifer that gives a positive, neutral, and negative response confidence percentage.\n",
    "\n",
    "See also: [Zhao et al. (2021)](https://arxiv.org/abs/2102.09690), [Liu et al. (2021)](https://arxiv.org/abs/2101.06804), [Su et al. (2022)](https://arxiv.org/abs/2209.01975), [Rubin et al. (2022)](https://arxiv.org/abs/2112.08633).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment(text):\n",
    "    response = chat_completion(messages=[\n",
    "        user(\"You are a sentiment classifier. For each message, give the percentage of positive/netural/negative.\"),\n",
    "        user(\"I liked it\"),\n",
    "        assistant(\"70% positive 30% neutral 0% negative\"),\n",
    "        user(\"It could be better\"),\n",
    "        assistant(\"0% positive 50% neutral 50% negative\"),\n",
    "        user(\"It's fine\"),\n",
    "        assistant(\"25% positive 50% neutral 25% negative\"),\n",
    "        user(text),\n",
    "    ])\n",
    "    return response\n",
    "\n",
    "def print_sentiment(text):\n",
    "    print(f'INPUT: {text}')\n",
    "    print(sentiment(text))\n",
    "\n",
    "print_sentiment(\"I thought it was okay\")\n",
    "# More likely to return a balanced mix of positive, neutral, and negative\n",
    "print_sentiment(\"I loved it!\")\n",
    "# More likely to return 100% positive\n",
    "print_sentiment(\"Terrible service 0/10\")\n",
    "# More likely to return 100% negative"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### Role Prompting\n",
    "\n",
    "Llama will often give more consistent responses when given a role ([Kong et al. (2023)](https://browse.arxiv.org/pdf/2308.07702.pdf)). Roles give context to the LLM on what type of answers are desired.\n",
    "\n",
    "Let's use Llama 3 to create a more focused, technical response for a question around the pros and cons of using PyTorch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\"Explain the pros and cons of using PyTorch.\")\n",
    "# More likely to explain the pros and cons of PyTorch covers general areas like documentation, the PyTorch community, and mentions a steep learning curve\n",
    "\n",
    "complete_and_print(\"Your role is a machine learning expert who gives highly technical advice to senior engineers who work with complicated datasets. Explain the pros and cons of using PyTorch.\")\n",
    "# Often results in more technical benefits and drawbacks that provide more technical details on how model layers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chain-of-Thought\n",
    "\n",
    "Simply adding a phrase encouraging step-by-step thinking \"significantly improves the ability of large language models to perform complex reasoning\" ([Wei et al. (2022)](https://arxiv.org/abs/2201.11903)). This technique is called \"CoT\" or \"Chain-of-Thought\" prompting.\n",
    "\n",
    "Llama 3.1 now reasons step-by-step naturally without the addition of the phrase. This section remains for completeness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"Who lived longer, Mozart or Elvis?\"\n",
    "\n",
    "complete_and_print(prompt)\n",
    "# Llama 2 would often give the incorrect answer of \"Mozart\"\n",
    "\n",
    "complete_and_print(f\"{prompt} Let's think through this carefully, step by step.\")\n",
    "# Gives the correct answer \"Elvis\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Self-Consistency\n",
    "\n",
    "LLMs are probablistic, so even with Chain-of-Thought, a single generation might produce incorrect results. Self-Consistency ([Wang et al. (2022)](https://arxiv.org/abs/2203.11171)) introduces enhanced accuracy by selecting the most frequent answer from multiple generations (at the cost of higher compute):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from statistics import mode\n",
    "\n",
    "def gen_answer():\n",
    "    response = completion(\n",
    "        \"John found that the average of 15 numbers is 40.\"\n",
    "        \"If 10 is added to each number then the mean of the numbers is?\"\n",
    "        \"Report the answer surrounded by backticks (example: `123`)\",\n",
    "    )\n",
    "    match = re.search(r'`(\\d+)`', response)\n",
    "    if match is None:\n",
    "        return None\n",
    "    return match.group(1)\n",
    "\n",
    "answers = [gen_answer() for i in range(5)]\n",
    "\n",
    "print(\n",
    "    f\"Answers: {answers}\\n\",\n",
    "    f\"Final answer: {mode(answers)}\",\n",
    "    )\n",
    "\n",
    "# Sample runs of Llama-3-70B (all correct):\n",
    "# ['60', '50', '50', '50', '50'] -> 50\n",
    "# ['50', '50', '50', '60', '50'] -> 50\n",
    "# ['50', '50', '60', '50', '50'] -> 50"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### Retrieval-Augmented Generation\n",
    "\n",
    "You'll probably want to use factual knowledge in your application. You can extract common facts from today's large models out-of-the-box (i.e. using just the model weights):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\"What is the capital of the California?\")\n",
    "# Gives the correct answer \"Sacramento\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, more specific facts, or private information, cannot be reliably retrieved. The model will either declare it does not know or hallucinate an incorrect answer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\"What was the temperature in Menlo Park on December 12th, 2023?\")\n",
    "# \"I'm just an AI, I don't have access to real-time weather data or historical weather records.\"\n",
    "\n",
    "complete_and_print(\"What time is my dinner reservation on Saturday and what should I wear?\")\n",
    "# \"I'm not able to access your personal information [..] I can provide some general guidance\""
   ]
  },
  {
   "attachments": {},
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   "source": [
    "Retrieval-Augmented Generation, or RAG, describes the practice of including information in the prompt you've retrived from an external database ([Lewis et al. (2020)](https://arxiv.org/abs/2005.11401v4)). It's an effective way to incorporate facts into your LLM application and is more affordable than fine-tuning which may be costly and negatively impact the foundational model's capabilities.\n",
    "\n",
    "This could be as simple as a lookup table or as sophisticated as a [vector database]([FAISS](https://github.com/facebookresearch/faiss)) containing all of your company's knowledge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MENLO_PARK_TEMPS = {\n",
    "    \"2023-12-11\": \"52 degrees Fahrenheit\",\n",
    "    \"2023-12-12\": \"51 degrees Fahrenheit\",\n",
    "    \"2023-12-13\": \"51 degrees Fahrenheit\",\n",
    "}\n",
    "\n",
    "\n",
    "def prompt_with_rag(retrived_info, question):\n",
    "    complete_and_print(\n",
    "        f\"Given the following information: '{retrived_info}', respond to: '{question}'\"\n",
    "    )\n",
    "\n",
    "\n",
    "def ask_for_temperature(day):\n",
    "    temp_on_day = MENLO_PARK_TEMPS.get(day) or \"unknown temperature\"\n",
    "    prompt_with_rag(\n",
    "        f\"The temperature in Menlo Park was {temp_on_day} on {day}'\",  # Retrieved fact\n",
    "        f\"What is the temperature in Menlo Park on {day}?\",  # User question\n",
    "    )\n",
    "\n",
    "\n",
    "ask_for_temperature(\"2023-12-12\")\n",
    "# \"Sure! The temperature in Menlo Park on 2023-12-12 was 51 degrees Fahrenheit.\"\n",
    "\n",
    "ask_for_temperature(\"2023-07-18\")\n",
    "# \"I'm not able to provide the temperature in Menlo Park on 2023-07-18 as the information provided states that the temperature was unknown.\""
   ]
  },
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   "attachments": {},
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   "metadata": {},
   "source": [
    "### Program-Aided Language Models\n",
    "\n",
    "LLMs, by nature, aren't great at performing calculations. Let's try:\n",
    "\n",
    "$$\n",
    "((-5 + 93 * 4 - 0) * (4^4 + -7 + 0 * 5))\n",
    "$$\n",
    "\n",
    "(The correct answer is 91383.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\"\"\"\n",
    "Calculate the answer to the following math problem:\n",
    "\n",
    "((-5 + 93 * 4 - 0) * (4^4 + -7 + 0 * 5))\n",
    "\"\"\")\n",
    "# Gives incorrect answers like 92448, 92648, 95463"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Gao et al. (2022)](https://arxiv.org/abs/2211.10435) introduced the concept of \"Program-aided Language Models\" (PAL). While LLMs are bad at arithmetic, they're great for code generation. PAL leverages this fact by instructing the LLM to write code to solve calculation tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\n",
    "    \"\"\"\n",
    "    # Python code to calculate: ((-5 + 93 * 4 - 0) * (4^4 + -7 + 0 * 5))\n",
    "    \"\"\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following code was generated by Llama 3 70B:\n",
    "\n",
    "result = ((-5 + 93 * 4 - 0) * (4**4 - 7 + 0 * 5))\n",
    "print(result)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limiting Extraneous Tokens\n",
    "\n",
    "A common struggle with Llama 2 is getting output without extraneous tokens (ex. \"Sure! Here's more information on...\"), even if explicit instructions are given to Llama 2 to be concise and no preamble. Llama 3.x can better follow instructions.\n",
    "\n",
    "Check out this improvement that combines a role, rules and restrictions, explicit instructions, and an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_and_print(\n",
    "    \"Give me the zip code for Menlo Park in JSON format with the field 'zip_code'\",\n",
    ")\n",
    "# Likely returns the JSON and also \"Sure! Here's the JSON...\"\n",
    "\n",
    "complete_and_print(\n",
    "    \"\"\"\n",
    "    You are a robot that only outputs JSON.\n",
    "    You reply in JSON format with the field 'zip_code'.\n",
    "    Example question: What is the zip code of the Empire State Building? Example answer: {'zip_code': 10118}\n",
    "    Now here is my question: What is the zip code of Menlo Park?\n",
    "    \"\"\",\n",
    ")\n",
    "# \"{'zip_code': 94025}\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional References\n",
    "- [PromptingGuide.ai](https://www.promptingguide.ai/)\n",
    "- [LearnPrompting.org](https://learnprompting.org/)\n",
    "- [Lil'Log Prompt Engineering Guide](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author & Contact\n",
    "\n",
    "Edited by [Dalton Flanagan](https://www.linkedin.com/in/daltonflanagan/) (dalton@meta.com) with contributions from Mohsen Agsen, Bryce Bortree, Ricardo Juan Palma Duran, Kaolin Fire, Thomas Scialom."
   ]
  }
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