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Foundation: Vision Capabilities and Prompt Building Blocks

What ChatGPT Image Prompts Can Do (and Where They Struggle)

Prompt Engineering Essentials for Images

Techniques: Systematic Workflows for Reliable Image Analysis

Constraint-First Prompts for Accuracy and Safety

Step-by-Step Reasoning and Visual Evidence Grounding

Iterative Feedback Loops and Troubleshooting

Applications: Targeted Patterns and Templates

Templates for Common Image Tasks

Quality Assurance: Checklists and Rubrics

Practice: Hands-On Prompts and Mini-Projects

Guided Exercises with Feedback

Knowledge Checks and Course Wrap-Up

Quiz: Capabilities and Limitations

Quiz: Constraints and Safety Scaffolds

Quiz: Reasoning and Evidence Grounding

Quiz: Iterative Troubleshooting

Quiz: Application Templates and QA Rubrics

Course Survey

Turn images into reliable insights with Prompt Engineering for ChatGPT Images: Evaluation, Troubleshooting, and Iteration. This practical course teaches you how to design high-precision prompts for visual tasks—OCR, charts, UI screenshots, scene analysis, and multi-image comparisons—while managing model limitations and safety. You’ll learn evidence-driven prompting that grounds every claim in what’s actually visible: quote exact text, reference regions, and report confidence. We’ll cover constraint-first techniques (JSON schemas, vocabulary limits, refusal rules), step-by-step reasoning workflows, and systematic cross-checks that reduce hallucinations and omissions. You’ll diagnose failure modes (misread text, missed regions, overgeneralization) and iterate with targeted refinements, A/B tests, and prompt logs. Through hands-on exercises—like reconciling receipt totals, extracting KPIs from dashboards, and identifying warehouse hazards—you’ll build reusable templates and QA rubrics that scale. By the end, you’ll have a toolkit to create prompts that are accurate, auditable, and safe, plus checklists to ensure completeness and correctness under real-world constraints.

Prompt Engineering for ChatGPT Images: Evaluation, Troubleshooting, and Iteration

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            <p>Prompt Engineering for ChatGPT Images: Evaluation, Troubleshooting, and Iteration equips you to design image prompts that are accurate, safe, and reliably grounded in visual evidence. You will learn how to harness core vision capabilities—object recognition, scene understanding, OCR, layout parsing, and chart/table interpretation—while anticipating known limitations such as small text, occlusions, noisy inputs, and hallucinations. From the outset, we frame success in terms of correctness, completeness, explicit uncertainty, and traceability to specific regions or quoted text in the image, with ethical guardrails for sensitive content, PII, bias, and consent.</p>

<p>The course begins with foundational building blocks for image prompting: setting roles and domain constraints, crafting clear objectives and refusal policies, and using evidence-driven instructions that reference regions and annotations. You will adopt decomposition strategies (detect → extract → interpret → decide) and ambiguity handling techniques, including clarifying questions and calibrated confidence reporting. We then move into systematic workflows, emphasizing constraint-first prompts, strict formatting (e.g., JSON schemas or bullet checklists), grounding requirements, and safety scaffolds. You will practice step-by-step reasoning, quadrant sweeps to avoid omissions, cross-check loops against OCR text, and disciplined confidence statements that note what evidence could change a conclusion.</p>

<p>To make your prompts robust, you will develop iterative feedback loops that diagnose failure modes—misreads, missed regions, overgeneralization, or hallucinated objects—and apply refinement tactics like tightening constraints, adding negatives, and demanding citations to image evidence. You will learn to produce concise error reports, request follow-up zooms or images, and version prompts for A/B testing with a prompt log. Application-focused templates translate these skills to real tasks: document/form OCR with validation, chart and dashboard extraction with units and timeframes, UI screenshot analysis for features and bugs, real-world scene assessments for hazards and relationships, and before/after comparisons for change detection.</p>

<p>Hands-on practice cements these methods through guided exercises: reconciling receipt totals from OCR, extracting KPIs and anomalies from a dashboard, and performing a warehouse safety scan with precise region references. A peer review protocol encourages rubric-based scoring and revision. Quality assurance tools—Definitions of Done, graded rubrics, minimal reproducible prompts, and human-in-the-loop escalation—ensure your outputs are auditable and reproducible. Knowledge checks throughout the course assess your grasp of capabilities, constraints, reasoning, troubleshooting, templates, and QA, culminating in a short survey to help improve future iterations. By the end, you will be able to design, evaluate, and iterate image prompts that are both high-accuracy and high-integrity.</p>
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