Generative AI Without Advanced Math

Distinguish rule-based software, traditional machine learning, and generative AI from their inputs and outputs.

Match common AI project tasks to the roles of data scientist, machine-learning engineer, and generative-AI application developer.

Store a model API key in an environment variable without exposing it in Python source code.

Send a single request to a large language model from Python and print its response.

Construct a prompt containing a clear task, relevant context, and explicit constraints.

Test a prompt against a small set of examples and record where its responses fail.

Implement a command-line AI assistant that preserves conversation history.

Use Python to evaluate arithmetic expressions involving addition, subtraction, multiplication, and division.

Use a Python variable as a placeholder and determine the value that satisfies a simple equality.

Convert a count into a fraction, decimal, and percentage.

Calculate and interpret the arithmetic mean of a small dataset with Python.

Calculate and interpret the median of a small dataset with Python.

Interpret a probability score as a value between 0 and 1 and as a percentage.

Read coordinates and numerical scales from a simple data chart.

Represent an input-output relationship with a Python function and a table of values.

Formulate a practical prediction question as either a classification task or a regression task.

Separate a tabular dataset into input features and a target variable.

Split a dataset into training and test sets and explain the purpose of each set.

Fit a linear regression model with scikit-learn and generate predictions.

Calculate mean absolute error from regression predictions and actual values.

Fit a decision-tree classifier with scikit-learn and generate predicted classes.

Construct and interpret an accuracy score and confusion matrix for a classifier.

Compare training and test performance to identify basic evidence of overfitting.

Explain how token-by-token text generation can produce fluent but incorrect answers.

Measure a prompt's token usage and shorten it to fit within a model's context limit.

Create a reusable prompt template whose values are supplied by Python variables.

Request structured JSON output from a model and validate the returned fields.

Handle a model API timeout or rate-limit error without crashing the application.

Add an instruction that makes the model state when supplied evidence is insufficient.

Build a simple Streamlit interface for a generative-AI text-processing application.

Evaluate the application on a fixed set of cases and revise one recurring failure.

Distinguish information learned during model training from context supplied at request time.

Extract and clean text from a document for use in a question-answering system.

Split document text into retrievable chunks and compare two chunk-size choices.

Generate text embeddings and explain semantic proximity using a simple coordinate analogy.

Retrieve the most relevant document chunks for a question using vector similarity search.

Construct a prompt that requires an answer and citations from retrieved context.

Make the application decline to answer when retrieved evidence is insufficient.

Evaluate retrieval relevance and answer faithfulness with a small question set.

Distinguish a deterministic workflow, a tool-calling assistant, and an autonomous agent.

Describe a Python function as an AI-callable tool using a structured argument schema.

Make a model select an appropriate tool for a user request.

Validate model-produced tool arguments before executing a Python function.

Execute only an allow-listed tool and return its result to the model.

Maintain explicit state across the steps of a multi-step AI workflow.

Limit tool usage with step limits, timeouts, and confirmation rules.

Test a tool-using workflow against successful, invalid, and unsafe requests.

Package a selected project as a reproducible Python repository with declared dependencies.

Deploy the selected project as a small web application on a hosted service.

Configure deployment secrets without committing credentials to source control.

Log request outcomes, latency, and token usage for a deployed AI application.

Estimate monthly model cost from request volume and average token usage.

Test the deployed application for basic prompt-injection and sensitive-data leakage risks.

Write a concise portfolio case study describing the problem, design choices, evaluation, and limitations.

Compare firsthand project tasks with AI role profiles and select a focused next learning path.