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Best prompts for LIM using problem-solving techniques using LIM
Thirteen copy-ready prompts that combine language-image model (LIM) capabilities with structured problem-solving techniques. Each prompt asks the model to observe an image, generate hypotheses, evaluate options, and give prioritized, actionable recommendations with confidence estimates. Use {IMAGE} for the image input and {TEXT} for any extra context.
GPT-5
Claude Opus 4
Claude Sonnet 4
Gemini 2.5 Pro
Gemini 2.5 Flash
You've spent hours staring at images, trying to squeeze actionable insights from your language-image model, only to get vague descriptions that don't help you solve real problems. You know the AI can "see" what's in the photo, but getting it to think systematically and give you structured, decision-ready analysis feels impossible. Sound familiar?
These 13 copy-ready prompts transform your LIM from a basic image describer into a structured problem-solving partner that delivers prioritized recommendations with confidence levels. From diagnosing product defects and auditing safety compliance to extracting quantitative data from charts and creating troubleshooting decision trees, each prompt guides the AI through systematic analysis that produces actionable results you can immediately use. No more generic observations - just clear, prioritized solutions that help you make confident decisions faster.
1
Identify and Prioritize Visible Faults
Given {IMAGE} and optional {TEXT}, list all visible faults or anomalies, rank them by likely impact (high/medium/low), provide evidence from the image for each rank, propose one immediate mitigation and one long-term fix for the top two faults, and give a confidence score (0-100%) for each finding.
Detect visible problems, prioritize by impact, and propose immediate and long-term fixes with evidence and confidence levels.
2
Stepwise Root-Cause Hypothesis Generation
Using {IMAGE} and {TEXT}, produce 3 plausible root-cause hypotheses for the observed issue. For each hypothesis: (a) list 3 image-based clues supporting it, (b) propose 2 diagnostic tests or measurements to disambiguate hypotheses, (c) estimate the likelihood (0-100%). Return results as three numbered hypothesis blocks.
Generate and support multiple root-cause hypotheses from visual clues, and recommend tests to confirm them.
3
Extract Quantitative Data from Charts and Compute
Given {IMAGE} of a chart (bar/line/heatmap) and optional {TEXT}, extract numerical values for labeled series (list as table: label, x, y), detect trends (increasing/decreasing/seasonal), compute the slope or percent change for main series, and identify any outliers (point, value, reason based on axes/labels).
Turn visual chart data into structured numeric output, quantify trends, and call out outliers with reasoning tied to axes/labels.
4
Annotate Blueprint or Diagram into Action Steps
Analyze {IMAGE} blueprint/diagram and transform it into a numbered set of concrete action steps needed to implement the design. For each step include: required resources, estimated time (short/medium/long), a potential risk and a mitigation, and a short justification tied to the image region (reference coordinates or labeled features).
Convert a visual plan into a prioritized implementation checklist with resources, time estimates, risks, and image references.
5
Diagnose Product Defect from Photos
Given up to 3 images showing a failed product and {TEXT} describing symptoms, identify likely defect types (manufacturing, design, wear, misuse), list the top 4 pieces of visual evidence supporting each defect type, recommend immediate triage steps and one test to confirm the defect classification, and label each recommendation with priority (P1/P2/P3).
Diagnose probable defect class from photos, cite visual evidence, and recommend prioritized triage and tests.
6
Convert Handwritten Notes and Sketches to Structured Plan
Using {IMAGE} of handwritten notes/sketches and optional {TEXT}, transcribe legible text, extract 6 key action items, infer missing specifics (e.g., quantities, deadlines) with explicit assumptions, and output a 1-week schedule assigning owners (use placeholders if unknown) and deliverables for each day.
Turn informal handwritten artifacts into a structured, timeboxed plan with explicit assumptions for any inferred details.
7
Design Iterations from UI Screenshot and Goals
Given {IMAGE} of a UI and {TEXT} stating product goals (e.g., increase conversion, reduce clicks), propose 4 specific design iterations. For each iteration provide: change description, expected effect (with metric to track), low-fi mock annotation describing where to change in the image, and an A/B test plan (sample size suggestion and success criteria).
Produce targeted UI changes with measurable hypotheses and simple A/B test plans tied to the screenshot regions.
Stop Losing Your AI Work
Tired of rewriting the same prompts, juggling ChatGPT and Claude in multiple tabs, and watching your best AI conversations disappear forever?
AI Flow Chat lets you save winning prompts to a reusable library, test all models in one workspace, and convert great chats into automated workflows that scale.
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