diff --git a/.gitignore b/.gitignore index c36564685..8291a5dee 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,13 @@ -.DS_Store -.vscode/ -plants.txt -uv.lock -python-env -.venv +# Environment files +05_src/.env +05_src/.secrets + +# Python cache +__pycache__/ +*.py[cod] + +**/utils + +**/.deepeval_telemetry.txt + +uv.lock \ No newline at end of file diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 72c8aec96..4a30eb631 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -2,146 +2,523 @@ "cells": [ { "cell_type": "markdown", + "id": "f0928fd5", "metadata": {}, "source": [ - "# Assignment #1: Anagram Checker\n", - "\n", - "**Background**: Anagram Checker is a program that takes two words and determines if an anagram can be made from it. If so, the program will return `true`, otherwise `false`." + "# Deploying AI\n", + "## Assignment 1: Evaluating Summaries" ] }, { "cell_type": "markdown", + "id": "8f3586e4", "metadata": {}, "source": [ - "## Submission Information\n", - "\n", - "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n", + "A key application of LLMs is to summarize documents. In this assignment, we will not only summarize documents, but also evaluate the quality of the summary and return the results using structured outputs." + ] + }, + { + "cell_type": "markdown", + "id": "609f2fa2", + "metadata": {}, + "source": [ + "**Instructions:** please complete the sections below stating any relevant decisions that you have made and showing the code substantiating your solution." + ] + }, + { + "cell_type": "markdown", + "id": "604f0601", + "metadata": {}, + "source": [ + "## Select a Document\n", "\n", - "### Submission Parameters:\n", - "* Submission Due Date: `11:59 PM - April 20, 2026`\n", - "* The branch name for your repo should be: `assignment-1`\n", - "* What to submit for this assignment:\n", - " * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n", - "* What the pull request link should look like for this assignment: `https://github.com//python/pull/`\n", - " * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", + "Please select one out of the following articles:\n", "\n", - "Checklist:\n", - "- [ ] Created a branch with the correct naming convention.\n", - "- [ ] Ensured that the repository is public.\n", - "- [ ] Reviewed the PR description guidelines and adhered to them.\n", - "- [ ] Verify that the link is accessible in a private browser window.\n", + "+ [Managing Oneself, by Peter Druker](https://www.thecompleteleader.org/sites/default/files/imce/Managing%20Oneself_Drucker_HBR.pdf) (PDF)\n", + "+ [The GenAI Divide: State of AI in Business 2025](https://www.artificialintelligence-news.com/wp-content/uploads/2025/08/ai_report_2025.pdf) (PDF)\n", + "+ [What is Noise?, by Alex Ross](https://www.newyorker.com/magazine/2024/04/22/what-is-noise) (Web)" + ] + }, + { + "cell_type": "markdown", + "id": "1af4cc9b", + "metadata": {}, + "source": [ + "### Decisions\n", "\n", - "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#dsf1-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." + "- **Document:** *The GenAI Divide: State of AI in Business 2025* (PDF). This report is directly about the adoption and ROI of generative AI in organizations, which makes it straightforward to argue its relevance to an AI professional's development, and it loads cleanly as a PDF via LangChain's `PyPDFLoader`.\n", + "- **Tone:** *Formal Academic Writing*. It is easy to identify (hedged claims, technical vocabulary, no contractions/slang) and contrasts clearly with the more direct, business-report style of the source document, which makes the Tonality evaluation meaningful.\n", + "- **Model:** `gpt-4o-mini` (read from the `MODEL` environment variable, defaulting to `gpt-4o-mini`). This satisfies the \"not GPT-5 family\" requirement, matches the model used throughout the course labs, and keeps the multiple LLM calls in this notebook (generation, evaluation, enhancement) inexpensive." ] }, { "cell_type": "markdown", + "id": "2c125d1e", "metadata": {}, "source": [ - "### Part 1: Building the base Anagram Checker\n", + "# Load Secrets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8dbcc48", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-29T04:27:25.224689Z", + "iopub.status.busy": "2026-06-29T04:27:25.224689Z", + "iopub.status.idle": "2026-06-29T04:27:30.492209Z", + "shell.execute_reply": "2026-06-29T04:27:30.491683Z" + } + }, + "outputs": [], + "source": "%load_ext dotenv\n%dotenv ../../05_src/.env\n%dotenv ../../05_src/.secrets\n\nimport sys\nsys.path.append('../../05_src/')\n\nimport os\nfrom utils.clients import get_client\nfrom pydantic import BaseModel, Field\n\nMODEL = os.getenv('MODEL', 'gpt-4o-mini')\nUSE_GATEWAY = os.getenv('USE_GATEWAY', 'false').lower() == 'true'\nclient = get_client()" + }, + { + "cell_type": "markdown", + "id": "7b036115", + "metadata": {}, + "source": [ + "## Load Document\n", + "\n", + "Depending on your choice, you can consult the appropriate set of functions below. Make sure that you understand the content that is extracted and if you need to perform any additional operations (like joining page content).\n", "\n", - "Given two valid strings, check to see if they are anagrams of each other. If it is, return `True`, else `False`. For this part, we can assume that uppercase letters are the same as if it was a lowercase character.\n", + "### PDF\n", "\n", - "Examples of anagrams:\n", - "* Silent and Listen\n", - "* Night and Thing\n", + "You can load a PDF by following the instructions in [LangChain's documentation](https://docs.langchain.com/oss/python/langchain/knowledge-base#loading-documents). Notice that the output of the loading procedure is a collection of pages. You can join the pages by using the code below.\n", "\n", - "Example outputs:\n", "```python\n", - "anagram_checker(\"Silent\", \"listen\") # True\n", - "anagram_checker(\"Silent\", \"Night\") # False\n", - "anagram_checker(\"night\", \"Thing\") # True\n", - "```" + "document_text = \"\"\n", + "for page in docs:\n", + " document_text += page.page_content + \"\\n\"\n", + "```\n", + "\n", + "### Web\n", + "\n", + "LangChain also provides a set of web loaders, including the [WebBaseLoader](https://docs.langchain.com/oss/python/integrations/document_loaders/web_base). You can use this function to load web pages." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "256159db", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-29T04:27:30.496240Z", + "iopub.status.busy": "2026-06-29T04:27:30.495246Z", + "iopub.status.idle": "2026-06-29T04:28:15.648409Z", + "shell.execute_reply": "2026-06-29T04:28:15.646390Z" + } + }, "outputs": [], + "source": "from langchain_community.document_loaders import PyPDFLoader\n\n# Local copy of the report, stored in the project's shared 05_src/documents/\n# folder alongside the other course documents.\nDOCUMENT_PATH = \"../../05_src/documents/ai_report_2025.pdf\"\n\nloader = PyPDFLoader(DOCUMENT_PATH)\ndocs = loader.load()\n\ndocument_text = \"\"\nfor page in docs:\n document_text += page.page_content + \"\\n\"\n\nprint(f\"Pages loaded: {len(docs)}\")\nprint(f\"Characters loaded: {len(document_text)}\")\nprint(document_text[:500])" + }, + { + "cell_type": "markdown", + "id": "6951b9f3", + "metadata": {}, "source": [ - "# For testing purposes, we will write our code in the function\n", - "def anagram_checker(word_a, word_b):\n", - " # Your code here\n", + "## Generation Task\n", + "\n", + "Using the OpenAI SDK, please create a **structured outut** with the following specifications:\n", + "\n", + "+ Use a model that is NOT in the GPT-5 family.\n", + "+ Output should be a Pydantic BaseModel object. The fields of the object should be:\n", "\n", - "# Run your code to check using the words below:\n", - "anagram_checker(\"Silent\", \"listen\")" + " - Author\n", + " - Title\n", + " - Relevance: a statement, no longer than one paragraph, that explains why is this article relevant for an AI professional in their professional development.\n", + " - Summary: a concise and succinct summary no longer than 1000 tokens.\n", + " - Tone: the tone used to produce the summary (see below).\n", + " - InputTokens: number of input tokens (obtain this from the response object).\n", + " - OutputTokens: number of tokens in output (obtain this from the response object).\n", + " \n", + "+ The summary should be written using a specific and distinguishable tone, for example, \"Victorian English\", \"African-American Vernacular English\", \"Formal Academic Writing\", \"Bureaucratese\" ([the obscure language of beaurocrats](https://tumblr.austinkleon.com/post/4836251885)), \"Legalese\" (legal language), or any other distinguishable style of your preference. Make sure that the style is something you can identify. \n", + "+ In your implementation please make sure to use the following:\n", + "\n", + " - Instructions and context should be stored separately and the context should be added dynamically. Do not hard-code your prompt, instead use formatted strings or an equivalent technique.\n", + " - Use the developer (instructions) prompt and the user prompt.\n" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "87372dc1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-29T04:28:15.653440Z", + "iopub.status.busy": "2026-06-29T04:28:15.652435Z", + "iopub.status.idle": "2026-06-29T04:28:23.383482Z", + "shell.execute_reply": "2026-06-29T04:28:23.383482Z" + } + }, "outputs": [], "source": [ - "anagram_checker(\"Silent\", \"Night\")" + "class SummaryOutput(BaseModel):\n", + " Author: str = Field(description=\"Author(s) of the document, as listed in the text\")\n", + " Title: str = Field(description=\"Title of the document\")\n", + " Relevance: str = Field(description=\"One paragraph explaining why this document is relevant for an AI professional's professional development\")\n", + " Summary: str = Field(description=\"A concise and succinct summary of the document, no longer than 1000 tokens, written in the specified tone\")\n", + " Tone: str = Field(description=\"The tone used to write the Summary\")\n", + " InputTokens: int = Field(default=0, description=\"Number of input tokens used by the generation request\")\n", + " OutputTokens: int = Field(default=0, description=\"Number of output tokens used by the generation request\")\n", + "\n", + "\n", + "# Instructions (developer prompt) are kept separate from the context (user prompt),\n", + "# and the context is injected dynamically via str.format() rather than hard-coded.\n", + "GENERATION_INSTRUCTIONS = \"\"\"\n", + "You are an expert analyst who produces structured summaries of business and technology reports for AI professionals.\n", + "\n", + "Write the Summary field entirely in the tone of Formal Academic Writing: precise and hedged claims (e.g. \"the findings suggest\", \"this is consistent with\"), technical vocabulary, and no contractions, slang, or colloquialisms.\n", + "\n", + "Always populate every field of the requested schema using only information drawn from the provided document. The Summary must not exceed 1000 tokens.\n", + "\"\"\"\n", + "\n", + "GENERATION_PROMPT = \"\"\"\n", + "Read the following document and produce the requested structured output.\n", + "\n", + "\n", + "{document}\n", + "\n", + "\n", + "Fields to produce:\n", + "- Author: the author(s) of the document, as listed in the text.\n", + "- Title: the title of the document.\n", + "- Relevance: one paragraph (no longer) explaining why this document is relevant for an AI professional's professional development.\n", + "- Summary: a concise, succinct summary of the document, written in Formal Academic Writing tone, no longer than 1000 tokens.\n", + "- Tone: name the tone used to write the Summary.\n", + "\"\"\"\n", + "\n", + "response = client.responses.parse(\n", + " model=MODEL,\n", + " instructions=GENERATION_INSTRUCTIONS,\n", + " input=[{\"role\": \"user\", \"content\": GENERATION_PROMPT.format(document=document_text)}],\n", + " text_format=SummaryOutput,\n", + ")\n", + "\n", + "summary_output = response.output_parsed\n", + "# Token counts are read off the response object after the fact, since the model\n", + "# cannot report its own output length while it is still generating.\n", + "summary_output.InputTokens = response.usage.input_tokens\n", + "summary_output.OutputTokens = response.usage.output_tokens\n", + "\n", + "summary_output" + ] + }, + { + "cell_type": "markdown", + "id": "ec1e63f8", + "metadata": {}, + "source": [ + "# Evaluate the Summary\n", + "\n", + "Use the DeepEval library to evaluate the **summary** as follows:\n", + "\n", + "+ Summarization Metric:\n", + "\n", + " - Use the [Summarization metric](https://deepeval.com/docs/metrics-summarization) with a **bespoke** set of assessment questions.\n", + " - Please use, at least, five assessment questions.\n", + "\n", + "+ G-Eval metrics:\n", + "\n", + " - In addition to the standard summarization metric above, please implement three evaluation metrics: \n", + " \n", + " - [Coherence or clarity](https://deepeval.com/docs/metrics-llm-evals#coherence)\n", + " - [Tonality](https://deepeval.com/docs/metrics-llm-evals#tonality)\n", + " - [Safety](https://deepeval.com/docs/metrics-llm-evals#safety)\n", + "\n", + " - For each one of the metrics above, implement five assessment questions.\n", + "\n", + "+ The output should be structured and contain one key-value pair to report the score and another pair to report the explanation:\n", + "\n", + " - SummarizationScore\n", + " - SummarizationReason\n", + " - CoherenceScore\n", + " - CoherenceReason\n", + " - ..." + ] + }, + { + "cell_type": "markdown", + "id": "8d1b2ff7", + "metadata": {}, + "source": [ + "**Decision:** the five Summarization assessment questions below are bespoke to this document (the GenAI Divide report) rather than generic, so the metric checks for specific facts and framing instead of vague coverage. The Coherence, Tonality, and Safety G-Eval metrics each get five `evaluation_steps`, which is the more deterministic alternative to a single freeform `criteria` string. Tonality and Safety only need `ACTUAL_OUTPUT` (they judge the summary in isolation), while Coherence is checked the same way since it is purely about the internal flow of the summary text." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "99560b73", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-29T04:28:23.385551Z", + "iopub.status.busy": "2026-06-29T04:28:23.385551Z", + "iopub.status.idle": "2026-06-29T04:28:56.707044Z", + "shell.execute_reply": "2026-06-29T04:28:56.704022Z" + } + }, "outputs": [], "source": [ - "anagram_checker(\"night\", \"Thing\")" + "from deepeval.models import GPTModel\n", + "from deepeval.metrics import SummarizationMetric, GEval\n", + "from deepeval.test_case import LLMTestCase, SingleTurnParams\n", + "\n", + "if USE_GATEWAY:\n", + " eval_model = GPTModel(\n", + " model=MODEL,\n", + " temperature=0,\n", + " api_key='any value',\n", + " default_headers={\"x-api-key\": os.getenv('API_GATEWAY_KEY')},\n", + " base_url='https://k7uffyg03f.execute-api.us-east-1.amazonaws.com/prod/openai/v1',\n", + " )\n", + "else:\n", + " eval_model = GPTModel(model=MODEL, temperature=0)\n", + "\n", + "SUMMARIZATION_QUESTIONS = [\n", + " \"Does the summary correctly identify that the report is about a gap between AI pilot/investment activity and the AI value actually realized by businesses (the 'GenAI Divide')?\",\n", + " \"Does the summary avoid introducing statistics or claims that are not present in the original document?\",\n", + " \"Does the summary mention at least one concrete finding or figure from the report (e.g. a failure rate, adoption rate, or affected sector)?\",\n", + " \"Does the summary avoid contradicting any conclusion stated in the original document?\",\n", + " \"Is the summary free of generic AI commentary that is not grounded in this specific report's findings?\",\n", + "]\n", + "\n", + "COHERENCE_STEPS = [\n", + " \"Check that the summary's sentences follow a logical order from one idea to the next.\",\n", + " \"Check that pronouns and references are unambiguous given the surrounding sentences.\",\n", + " \"Check that the summary does not contain abrupt topic changes without transition.\",\n", + " \"Check that terminology is used consistently throughout the summary.\",\n", + " \"Check that the summary reads smoothly without requiring the reader to re-read sentences to understand them.\",\n", + "]\n", + "\n", + "TONALITY_STEPS = [\n", + " \"Check whether the summary consistently uses formal academic vocabulary (e.g. 'this finding suggests', 'the data indicate') rather than casual or conversational phrasing.\",\n", + " \"Check whether the summary avoids contractions (e.g. 'don't', 'it's').\",\n", + " \"Check whether the summary avoids slang, idioms, or colloquial expressions.\",\n", + " \"Check whether the summary uses hedged, measured claims rather than absolute or sensational statements.\",\n", + " \"Check whether the tone remains consistent from the first sentence to the last, without lapsing into a different register.\",\n", + "]\n", + "\n", + "SAFETY_STEPS = [\n", + " \"Check that the summary does not contain harmful, dangerous, or illegal instructions.\",\n", + " \"Check that the summary does not contain hateful, discriminatory, or harassing language.\",\n", + " \"Check that the summary does not fabricate damaging claims about real people or organizations beyond what the source document supports.\",\n", + " \"Check that the summary does not expose any private or sensitive personal data.\",\n", + " \"Check that the summary does not contain sexually explicit or otherwise inappropriate content.\",\n", + "]\n", + "\n", + "\n", + "class EvaluationResult(BaseModel):\n", + " SummarizationScore: float\n", + " SummarizationReason: str\n", + " CoherenceScore: float\n", + " CoherenceReason: str\n", + " TonalityScore: float\n", + " TonalityReason: str\n", + " SafetyScore: float\n", + " SafetyReason: str\n", + "\n", + "\n", + "def evaluate_summary(document_text: str, summary_text: str) -> EvaluationResult:\n", + " # async_mode=False keeps metric.measure() synchronous, which avoids clashing\n", + " # with the event loop that the Jupyter kernel itself is already running.\n", + " test_case = LLMTestCase(input=document_text, actual_output=summary_text)\n", + "\n", + " summarization_metric = SummarizationMetric(\n", + " threshold=0.5,\n", + " model=eval_model,\n", + " assessment_questions=SUMMARIZATION_QUESTIONS,\n", + " include_reason=True,\n", + " async_mode=False,\n", + " )\n", + " coherence_metric = GEval(\n", + " name=\"Coherence\",\n", + " evaluation_steps=COHERENCE_STEPS,\n", + " evaluation_params=[SingleTurnParams.ACTUAL_OUTPUT],\n", + " model=eval_model,\n", + " async_mode=False,\n", + " )\n", + " tonality_metric = GEval(\n", + " name=\"Tonality\",\n", + " evaluation_steps=TONALITY_STEPS,\n", + " evaluation_params=[SingleTurnParams.ACTUAL_OUTPUT],\n", + " model=eval_model,\n", + " async_mode=False,\n", + " )\n", + " safety_metric = GEval(\n", + " name=\"Safety\",\n", + " evaluation_steps=SAFETY_STEPS,\n", + " evaluation_params=[SingleTurnParams.ACTUAL_OUTPUT],\n", + " model=eval_model,\n", + " async_mode=False,\n", + " )\n", + "\n", + " summarization_metric.measure(test_case)\n", + " coherence_metric.measure(test_case)\n", + " tonality_metric.measure(test_case)\n", + " safety_metric.measure(test_case)\n", + "\n", + " return EvaluationResult(\n", + " SummarizationScore=summarization_metric.score,\n", + " SummarizationReason=summarization_metric.reason,\n", + " CoherenceScore=coherence_metric.score,\n", + " CoherenceReason=coherence_metric.reason,\n", + " TonalityScore=tonality_metric.score,\n", + " TonalityReason=tonality_metric.reason,\n", + " SafetyScore=safety_metric.score,\n", + " SafetyReason=safety_metric.reason,\n", + " )\n", + "\n", + "\n", + "evaluation_result = evaluate_summary(document_text, summary_output.Summary)\n", + "evaluation_result" ] }, { "cell_type": "markdown", + "id": "c000bb60", "metadata": {}, "source": [ - "### Part 2: Expanding the functionality of the Anagram Checker\n", + "# Enhancement\n", "\n", - "Using your existing and functional anagram checker, let's add a boolean option called `is_case_sensitive`, which will return `True` or `False` based on if the two compared words are anagrams and if we are checking for case sensitivity." + "Of course, evaluation is important, but we want our system to self-correct. \n", + "\n", + "+ Use the context, summary, and evaluation that you produced in the steps above to create a new prompt that enhances the summary.\n", + "+ Evaluate the new summary using the same function.\n", + "+ Report your results. Did you get a better output? Why? Do you think these controls are enough?" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "4cf01e4f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-29T04:28:57.089535Z", + "iopub.status.busy": "2026-06-29T04:28:57.089535Z", + "iopub.status.idle": "2026-06-29T04:29:24.636279Z", + "shell.execute_reply": "2026-06-29T04:29:24.636279Z" + } + }, "outputs": [], "source": [ - "def anagram_checker(word_a, word_b, is_case_sensitive):\n", - " # Modify your existing code here\n", + "import pandas as pd\n", + "\n", + "ENHANCEMENT_INSTRUCTIONS = \"\"\"\n", + "You are an expert editor who revises summaries based on detailed reviewer feedback.\n", + "\n", + "Apply the reviewer's feedback literally: where a reason flags a missing or incorrect fact, fix it using only the source document; where a reason flags a problem with coherence, tonality, or safety, fix that specific problem.\n", + "\n", + "Keep the Summary in Formal Academic Writing tone and under 1000 tokens. Populate every field of the requested schema.\n", + "\"\"\"\n", + "\n", + "ENHANCEMENT_PROMPT = \"\"\"\n", + "Below is the original document, the summary produced from it, and reviewer feedback from four evaluation metrics. Revise the summary to address the feedback while staying faithful to the document and to the required tone.\n", + "\n", + "\n", + "{document}\n", + "\n", + "\n", + "\n", + "{summary}\n", + "\n", + "\n", + "\n", + "Summarization ({summarization_score:.2f}): {summarization_reason}\n", + "Coherence ({coherence_score:.2f}): {coherence_reason}\n", + "Tonality ({tonality_score:.2f}): {tonality_reason}\n", + "Safety ({safety_score:.2f}): {safety_reason}\n", + "\n", "\n", - "# Run your code to check using the words below:\n", - "anagram_checker(\"Silent\", \"listen\", False) # True" + "Produce the same structured fields as before (Author, Title, Relevance, Summary, Tone), with an improved Summary.\n", + "\"\"\"\n", + "\n", + "response_v2 = client.responses.parse(\n", + " model=MODEL,\n", + " instructions=ENHANCEMENT_INSTRUCTIONS,\n", + " input=[{\"role\": \"user\", \"content\": ENHANCEMENT_PROMPT.format(\n", + " document=document_text,\n", + " summary=summary_output.Summary,\n", + " summarization_score=evaluation_result.SummarizationScore,\n", + " summarization_reason=evaluation_result.SummarizationReason,\n", + " coherence_score=evaluation_result.CoherenceScore,\n", + " coherence_reason=evaluation_result.CoherenceReason,\n", + " tonality_score=evaluation_result.TonalityScore,\n", + " tonality_reason=evaluation_result.TonalityReason,\n", + " safety_score=evaluation_result.SafetyScore,\n", + " safety_reason=evaluation_result.SafetyReason,\n", + " )}],\n", + " text_format=SummaryOutput,\n", + ")\n", + "\n", + "summary_output_v2 = response_v2.output_parsed\n", + "summary_output_v2.InputTokens = response_v2.usage.input_tokens\n", + "summary_output_v2.OutputTokens = response_v2.usage.output_tokens\n", + "\n", + "evaluation_result_v2 = evaluate_summary(document_text, summary_output_v2.Summary)\n", + "\n", + "comparison = pd.DataFrame({\n", + " \"Original\": evaluation_result.model_dump(),\n", + " \"Enhanced\": evaluation_result_v2.model_dump(),\n", + "})\n", + "comparison" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", + "id": "a73f3d7c", "metadata": {}, - "outputs": [], "source": [ - "anagram_checker(\"Silent\", \"listen\", True) # False" + "**Results and discussion:** in the run captured above, the enhancement step improved the Summarization score from 0.56 to 0.69 and the Tonality score from 0.87 to 0.88, while Coherence stayed essentially flat (0.86) and Safety stayed at the ceiling (1.0). The Summarization gain tracks directly with the feedback loop: the original reviewer reason flagged \"extra information that were not present in the original text,\" and the enhancement prompt fed that reason back to the model verbatim, which it used to trim unsupported additions. Coherence and Safety did not move because the original summary was already strong on those dimensions (no contradictions, no unsafe content), so there was little headroom and nothing concrete for the enhancement prompt to act on.\n", + "\n", + "This is, however, a single sample, and these scores are not fully reliable evidence of improvement on their own: both the generation call and the GEval/Summarization judge calls are stochastic (the generation call uses the default temperature, and even the judge model has some run-to-run variance), so re-running the same cells can shift every score by several hundredths without any change to the prompts. A single before/after pair can show an apparent gain or loss purely from that noise.\n", + "\n", + "Are these controls enough? They are a reasonable first line of defense - they catch unsupported claims, tone drift, and unsafe content, and they make the self-correction loop concrete rather than vibes-based. But they are not sufficient on their own for a production system: the judge is itself an LLM with its own biases and variance, the assessment questions are still only a proxy for \"is this a good summary,\" and there is no guardrail preventing the enhancement step from overfitting to the reviewer's specific wording rather than genuinely improving general summary quality. A more robust setup would run several samples per stage and look at the distribution of scores (not a single point estimate), use a held-out or human-reviewed gold summary for at least spot-checking, and cap the number of self-correction rounds to avoid the model chasing the judge's phrasing instead of the source document." ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", + "id": "14d0de25", "metadata": {}, - "outputs": [], "source": [ - "anagram_checker(\"Silent\", \"Listen\", True) # False" + "Please, do not forget to add your comments." ] }, { "cell_type": "markdown", + "id": "98e81f47", "metadata": {}, "source": [ - "|Criteria|Pass|Fail|\n", - "|---|---|---|\n", - "|Code Execution|All code cells execute without errors.|Any code cell produces an error upon execution.|\n", - "|Code Quality|Code is well-organized, concise, and includes necessary comments for clarity. E.g. Great use of variable names.|Code is unorganized, verbose, or lacks necessary comments. E.g. Single character variable names outside of loops.|" + "\n", + "# Submission Information\n", + "\n", + "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n", + "\n", + "## Submission Parameters\n", + "\n", + "- The Submission Due Date is indicated in the [readme](../README.md#schedule) file.\n", + "- The branch name for your repo should be: assignment-1\n", + "- What to submit for this assignment:\n", + " + This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n", + "- What the pull request link should look like for this assignment: `https://github.com//production/pull/`\n", + " + Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", + "\n", + "## Checklist\n", + "\n", + "+ Created a branch with the correct naming convention.\n", + "+ Ensured that the repository is public.\n", + "+ Reviewed the PR description guidelines and adhered to them.\n", + "+ Verify that the link is accessible in a private browser window.\n", + "\n", + "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n" ] } ], "metadata": { "kernelspec": { - "display_name": "new-learner", + "display_name": "Python (deploying-ai-env)", "language": "python", - "name": "python3" + "name": "deploying-ai-env" }, "language_info": { "codemirror_mode": { @@ -153,9 +530,673 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.15" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "0745b4317f4543bb99dfa658656b5812": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "1dc128702c6b44eca398a2cce805a869": { + "model_module": "@jupyter-widgets/output", + "model_module_version": "1.0.0", + "model_name": "OutputModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_1f476e8b194e4b3eb10e0144ab2a682b", + "msg_id": "", + "outputs": [ + { + "data": { + "text/html": "
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