AI Professional

Construct a prompt that specifies the goal, context, audience, constraints, and expected output.

Use delimiters to separate instructions, source material, and user input in a prompt.

Request a predictable response structure using explicit fields, headings, or a schema.

Create a few-shot prompt that demonstrates the desired output with examples.

Decompose a complex task into a sequence of manageable prompts.

Diagnose why a prompt produced a weak response and revise the prompt to correct it.

Convert a successful prompt into a reusable template with clearly defined variables.

Produce a source-grounded summary of an uploaded document with traceable citations.

Compare claims from two documents in a table that highlights agreements, conflicts, and missing evidence.

Generate a beginner-friendly explanation that is accurate, appropriately scoped, and supported by an example.

Use Claude to turn a learning objective into a structured beginner lesson outline.

Use Claude to analyze an uploaded CSV and independently verify a reported result against the source data.

Configure a Claude Project with instructions and reference files to act as a reusable study assistant.

Create and test an interactive Claude Artifact that teaches or quizzes a beginner on one topic.

Identify hallucinations, unsupported claims, and misleading expressions of certainty in AI-generated content.

Verify an AI-generated factual claim using authoritative sources and record the evidence.

Construct a rubric for evaluating an AI output for accuracy, relevance, clarity, and completeness.

Create a small test set that reveals common failure modes in a prompt or AI assistant.

Detect biased assumptions or stereotypes in an AI response and revise the workflow to reduce them.

Protect personal and confidential information by redacting or replacing sensitive data before using an AI tool.

Apply attribution, copyright, and academic-integrity rules to a realistic student use of generative AI.

Recognize a prompt-injection attempt in external content and prevent it from overriding trusted instructions.

Differentiate Claude Pro from the Claude API in terms of access, billing, and suitable use cases.

Configure Google Colab to use the Anthropic SDK and an API key without exposing the key in notebook code.

Send a basic Claude API request in Python and extract the generated text from the response.

Adjust generation parameters and compare their effects on response length and variability.

Convert a fixed API prompt into a reusable Python function with validated user inputs.

Request structured JSON from Claude and parse the result in Python.

Diagnose and handle common authentication, rate-limit, and malformed-output errors.

Define the target learner, core task, and measurable success criterion for an AI learning-assistant application.

Connect a Python model-calling function to a basic Gradio input-and-output interface.

Add multi-turn conversation history to a Gradio learning assistant.

Enable the application to use learner-provided reference text while keeping instructions separate from that text.

Implement input and output checks that reduce unsafe, irrelevant, or privacy-sensitive behavior.

Test the application with normal, ambiguous, edge-case, and adversarial prompts.

Publish a working application with setup instructions, usage examples, limitations, and a short demonstration.

Translate an AI topic into a measurable learning objective suitable for a beginner session.

Sequence an online lesson from prior-knowledge activation through demonstration, guided practice, and assessment.

Explain a technical AI concept in plain language using an accurate example or analogy.

Prepare a live AI-tool demonstration with a clear script and a fallback for likely failures.

Design a guided practice activity and an aligned rubric for evaluating learner work.

Use polls, chat prompts, and targeted questions to check understanding during an online session.

Rehearse a short online AI lesson and revise it using a teaching-quality rubric.

Compare entry-level AI trainer, AI-literacy facilitator, AI enablement, and junior AI application roles and select a target role.

Extract recurring skills, tools, and evidence requirements from representative job descriptions for the target role.

Map existing portfolio evidence to job requirements and identify the highest-priority skill gaps.

Package a no-code Claude workflow as a portfolio case study describing the audience, problem, process, evaluation, and result.

Present the Python AI application in a public portfolio with a clear README, screenshots, and responsible-use notes.

Write targeted résumé bullets and a professional profile summary using evidence from completed projects and teaching experience.

Deliver a five-minute portfolio demonstration that addresses common technical and teaching-focused interview questions.