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Instead of writing code line by line, you have a conversation like: \u003Cem\u003E"Build me a dashboard," "Add a sidebar with filters," and "Make the chart interactive."\u003C/em\u003E AI then generates the code, you see the result, and you keep going until it's right.\u003C/p\u003E\n","\u003Cp\u003EStreamlit is the ideal framework for vibe coding because:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EVisual feedback loop\u003C/strong\u003E: Save the file, see the app update instantly. You know immediately if the AI got it right.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPattern-friendly\u003C/strong\u003E: Most Streamlit apps follow recognizable patterns (cache data, build layout, handle state). AI assistants excel at these.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELow boilerplate\u003C/strong\u003E: A working app can be 20 lines. This means fewer things for the AI to get wrong and faster iterations.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProgressive complexity\u003C/strong\u003E: Start simple, add features through conversation. Each prompt builds on what's already there.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis guide walks you through building a real data explorer app from scratch, no manual coding required. By the end, you'll know which prompt patterns actually work, what to do when the AI goes sideways, and how to go from a blank file to a polished app in under 30 minutes.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA data explorer app that lets users upload a CSV, view key metrics, visualize data with interactive charts, and handle edge cases gracefully — all built through iterative prompting.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe vibe coding workflow: describe, generate, see, refine\u003C/li\u003E\u003Cli\u003EHow to write effective prompts for Streamlit development\u003C/li\u003E\u003Cli\u003EWhen to iterate on a prompt vs. start fresh\u003C/li\u003E\u003Cli\u003EHow to handle edge cases and errors through conversation\u003C/li\u003E\u003Cli\u003EPrompt patterns that consistently produce good Streamlit code\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAI coding assistant (e.g. \u003Ca href=\"https://signup.snowflake.com/cortex-code?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake CoCo\u003C/a\u003E, Claude Code, Cursor, Gemini CLI, etc.) or cloud-based LLMs (e.g. ChatGPT, Claude, Gemini, etc.)\u003C/li\u003E\u003Cli\u003EPython 3 installed\u003C/li\u003E\u003Cli\u003EStreamlit installed (\u003Ccode\u003Epip install streamlit\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003ENo prior Streamlit experience needed. The AI handles the code\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe Vibe Coding Workflow\u003C/h2\u003E\n","\u003Cp\u003EBefore we start building, let's understand the workflow. Vibe coding follows a simple loop:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EDescribe\u003C/strong\u003E what you want in plain English\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGenerate\u003C/strong\u003E: the AI writes the code\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESee\u003C/strong\u003E: run the app and look at the result\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERefine\u003C/strong\u003E: tell the AI what to change\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYour role shifts from \u003Cem\u003E"programmer"\u003C/em\u003E to \u003Cem\u003E"product designer."\u003C/em\u003E You decide what the app should do and how it should feel. The AI handles implementation. You'll still glance at the code occasionally, mostly to make sure the AI didn't do something obviously wrong, but you'll spend 90% of your time describing and evaluating, not typing.\u003C/p\u003E\n","\u003Ch3\u003EWhen to Iterate vs. When to Restart\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIterate\u003C/strong\u003E when the app is 70%+ correct and needs tweaks\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERestart\u003C/strong\u003E when the AI went in a fundamentally wrong direction\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERule of thumb\u003C/strong\u003E: If your correction prompt is longer than the original prompt, consider starting over with a better initial description\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhy this matters: AI assistants build on their previous output. If the foundation is wrong (wrong architecture, wrong data flow, wrong assumptions about your data), each iteration digs the hole deeper. A fresh prompt with better context is faster than 10 correction prompts fighting a bad start.\u003C/p\u003E\n","\u003Ch3\u003EThe Prompt Spectrum\u003C/h3\u003E\n","\u003Cp\u003EPrompts exist on a spectrum from vague to specific:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EToo vague: \u003Cem\u003E"Make a dashboard"\u003C/em\u003E — AI guesses everything, probably wrong\u003C/li\u003E\u003Cli\u003EToo specific: \u003Cem\u003E"Create a st.columns(3) with st.metric in each, using border=True, with values from df['price'].sum()"\u003C/em\u003E — You're just dictating code\u003C/li\u003E\u003Cli\u003EJust right: \u003Cem\u003E"Build a dashboard with three KPI cards at the top showing revenue, orders, and customers. Add a line chart below for trends over time."\u003C/em\u003E — Clear intent, flexible implementation\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe First Prompt\u003C/h2\u003E\n","\u003Cp\u003ELet's start building. Open your AI coding assistant and type:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EBuild a Streamlit data explorer app. I want to upload a CSV file\nand see the data in a table. Keep it simple for now.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe AI generates something like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport streamlit as st\nimport pandas as pd\n\nst.set_page_config(page_title="Data Explorer", layout="centered")\nst.title("Data Explorer")\n\nuploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])\n\nif uploaded_file is not None:\n    df = pd.read_csv(uploaded_file)\n    st.dataframe(df)\nelse:\n    st.info("Upload a CSV file to get started.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Estreamlit run app.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see a file upload widget and, after uploading a CSV, a data table. It works, but it's bare-bones. That's fine. We'll build from here.\u003C/p\u003E\n","\u003Ch3\u003EWhat to Look For\u003C/h3\u003E\n","\u003Cp\u003EAfter the AI generates code, do a quick sanity check:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDoes it import streamlit?\u003C/li\u003E\u003Cli\u003EDoes it have \u003Ccode\u003Est.set_page_config()\u003C/code\u003E?\u003C/li\u003E\u003Cli\u003EDoes it use \u003Ccode\u003Est.file_uploader()\u003C/code\u003E with a sensible file type filter?\u003C/li\u003E\u003Cli\u003EDoes it handle the case where no file is uploaded yet?\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf any of these are missing, mention it in your next prompt. The AI will fix it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round1.png?v=c8f3e3b8\" alt=\"Round 1 app\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EImprove the Layout\u003C/h2\u003E\n","\u003Cp\u003EThe app works, but it's a single column with everything stacked. Now refine the structure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EMove the file upload to the sidebar. Show the number of rows and\ncolumns as metrics above the table. Use wide layout.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe AI restructures the app to something like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Est.set_page_config(page_title="Data Explorer", layout="wide")\n\nwith st.sidebar:\n    uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])\n\nst.title("Data Explorer")\n\nif uploaded_file is not None:\n    df = pd.read_csv(uploaded_file)\n    col1, col2 = st.columns(2)\n    col1.metric("Rows", df.shape[0])\n    col2.metric("Columns", df.shape[1])\n    st.dataframe(df)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice how we described what we wanted, not how to code it. We said \u003Cem\u003E"metrics above the table,"\u003C/em\u003E not \u003Cem\u003E"use \u003Ccode\u003Est.columns(2)\u003C/code\u003E with \u003Ccode\u003Est.metric\u003C/code\u003E inside."\u003C/em\u003E This is the core discipline of vibe coding: own the intent, delegate the implementation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round2.png?v=c8f3e3b8\" alt=\"Round 2 app\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdd Charts\u003C/h2\u003E\n","\u003Cp\u003EThe layout is clean. Now make the data visual:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAdd a chart section below the table. Let me pick the chart type\n(bar, line, scatter) and choose which columns to use for X and Y\naxes. Only show numeric columns for the Y axis.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe AI adds \u003Ccode\u003Est.selectbox\u003C/code\u003E widgets for chart type and column selection and filters columns by dtype. It will likely reach for Plotly or Altair for the charts. That works, but Streamlit's built-in chart functions are simpler and keep the dependency list short. A quick follow-up fixes that:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUse Streamlit's built-in chart functions instead of Plotly.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow the code uses native charts:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Enumeric_cols = df.select_dtypes(include="number").columns.tolist()\nall_cols = df.columns.tolist()\n\nchart_type = st.selectbox("Chart type", ["bar", "line", "scatter"])\nx_col = st.selectbox("X axis", all_cols)\ny_col = st.selectbox("Y axis", numeric_cols)\n\nif chart_type == "bar":\n    st.bar_chart(df.set_index(x_col)[y_col])\nelif chart_type == "line":\n    st.line_chart(df.set_index(x_col)[y_col])\nelif chart_type == "scatter":\n    st.scatter_chart(df, x=x_col, y=y_col)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is where vibe coding shines. Describing a feature like \u003Cem\u003E"only show numeric columns for Y axis"\u003C/em\u003E is one sentence for you but several lines of code for the AI. And when the AI picks a heavier library than you need, a single follow-up sentence is all it takes to course-correct.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round3.png?v=c8f3e3b8\" alt=\"Round 3 app\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStyle and Polish\u003C/h2\u003E\n","\u003Cp\u003EThe app is functional. Now make it feel finished:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAdd material icons to the section headers. Replace the generic row/column\nmetrics with meaningful ones: average price, total orders, total customers,\nand number of products. Add conditional formatting to the data table and\na download button.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe AI's first attempt will probably use emoji for icons and pull in a third-party table library. That is functional, but Streamlit has native solutions for all of these. Steer it with a follow-up:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUse Streamlit's :material/icon_name: syntax for icons instead of emoji.\nUse st.column_config for conditional formatting on st.dataframe.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf the result still has stray dependencies or emoji shortcodes, one more nudge finishes the job:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ERemove any third-party table dependencies. Use st.dataframe with the\nst.column_config you already defined. For icons, use the :material/icon_name:\nsyntax everywhere.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter these corrections the AI adds \u003Ccode\u003E:material/icon_name:\u003C/code\u003E syntax to headers, replaces generic metrics with data-specific ones, adds column configuration to the dataframe, and adds \u003Ccode\u003Est.download_button\u003C/code\u003E. Two rounds of feedback, each a few sentences, and the app now uses only built-in Streamlit features.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round4.png?v=c8f3e3b8\" alt=\"Round 4 app\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EHandle Edge Cases\u003C/h2\u003E\n","\u003Cp\u003EThe happy path works. Now think about what could go wrong:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHandle these edge cases:\n- No file uploaded yet: show a friendly message with upload instructions\n- File has no numeric columns: hide the chart section and explain why\n- File is empty: show a warning\n- Very large files: show a warning if over 100MB\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is one of the most underrated vibe coding patterns. You don't need to think about \u003Ccode\u003Eif/else\u003C/code\u003E logic or error handling code. Just describe what should happen when things go wrong, and the AI figures out the implementation. Plain English bullet points become guardrails.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round5.png?v=c8f3e3b8\" alt=\"Round 5 app\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrompt Patterns That Work\u003C/h2\u003E\n","\u003Cp\u003EAfter building the app across 5 rounds, here are the prompt patterns that consistently produce good results:\u003C/p\u003E\n","\u003Ch3\u003E1. Start Broad, Then Narrow\u003C/h3\u003E\n","\u003Cp\u003EStart with the big picture, then zoom into details in subsequent prompts. Don't try to specify everything in prompt one.\u003C/p\u003E\n","\u003Ch3\u003E2. Describe the User Experience\u003C/h3\u003E\n","\u003Cp\u003E\u003Cem\u003E"When the user clicks X, they should see Y"\u003C/em\u003E is more effective than \u003Cem\u003E"add an on_click callback that updates session state."\u003C/em\u003E Why: the AI knows Streamlit's event model better than most developers. Describing the desired behavior lets it pick the right mechanism (whether that's \u003Ccode\u003Est.session_state\u003C/code\u003E, a callback, or a simple re-run) rather than locking it into an approach that might not fit.\u003C/p\u003E\n","\u003Ch3\u003E3. Reference What Exists\u003C/h3\u003E\n","\u003Cp\u003E\u003Cem\u003E"In the sidebar, below the file upload, add a multiselect for columns."\u003C/em\u003E This tells the AI where to put things relative to what already exists.\u003C/p\u003E\n","\u003Ch3\u003E4. Show, Don't Tell (for Design)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cem\u003E"Make it look like a data dashboard with KPI cards at the top, filters on the left, and charts below"\u003C/em\u003E gives the AI a concrete reference.\u003C/p\u003E\n","\u003Ch3\u003E5. Batch Related Changes\u003C/h3\u003E\n","\u003Cp\u003EInstead of 5 separate prompts for 5 small changes, combine them: \u003Cem\u003E"Fix these issues: the chart title is cut off, add a divider between sections, move the download button below the chart, add tooltips to the metrics, and change the date format to MM/DD."\u003C/em\u003E\u003C/p\u003E\n","\u003Ch3\u003E6. The "What If" Pattern\u003C/h3\u003E\n","\u003Cp\u003E\u003Cem\u003E"What happens if someone uploads a file with 1 million rows? Or a file with special characters in column names?"\u003C/em\u003E This proactively surfaces edge cases.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECommon Pitfalls\u003C/h2\u003E\n","\u003Ch3\u003EOver-Specifying\u003C/h3\u003E\n","\u003Cp\u003ETelling the AI exactly which Streamlit functions to use defeats the purpose. You're coding with extra steps. Describe intent instead.\u003C/p\u003E\n","\u003Ch3\u003ENot Running Between Iterations\u003C/h3\u003E\n","\u003Cp\u003ERun the app after every prompt. Stacking 5 changes without running risks compounding errors that are hard to untangle. When something breaks after 5 unverified rounds, you won't know which prompt introduced the bug, and neither will the AI. Frequent runs give you a known-good checkpoint to describe from: \u003Cem\u003E"The chart was working after round 3, but now it's gone"\u003C/em\u003E is far more actionable than \u003Cem\u003E"something broke."\u003C/em\u003E If the app crashes, Streamlit shows the traceback directly in the browser. Paste that exact error message into your AI assistant as your next prompt. Don't paraphrase it; the raw traceback gives the AI the context it needs to fix the problem.\u003C/p\u003E\n","\u003Ch3\u003EIgnoring the AI's Code\u003C/h3\u003E\n","\u003Cp\u003EYou don't need to read every line, but do a 30-second scan after each round. Look for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Est.experimental_*\u003C/code\u003E functions, which are deprecated; prompt the AI: \u003Cem\u003E"Replace any deprecated \u003Ccode\u003Est.experimental_*\u003C/code\u003E calls with their current equivalents."\u003C/em\u003E\u003C/li\u003E\u003Cli\u003EHardcoded file paths like \u003Ccode\u003E/Users/yourname/...\u003C/code\u003E, which break on other machines.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Est.cache\u003C/code\u003E instead of \u003Ccode\u003Est.cache_data\u003C/code\u003E or \u003Ccode\u003Est.cache_resource\u003C/code\u003E. The old decorator was removed and will cause an error in current Streamlit versions.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EToo Many Changes at Once\u003C/h3\u003E\n","\u003Cp\u003EIf a prompt asks for 10 things and the AI gets 3 wrong, you don't know which prompts worked and can't isolate what to fix. Keep rounds focused: 2-4 changes per prompt. The exception is the "Batch Related Changes" pattern, where grouping 5 small visual tweaks into one prompt is fine because they're independent and low-risk. The rule is about avoiding large, interdependent changes in a single round.\u003C/p\u003E\n","\u003Ch3\u003ENot Saving Checkpoints\u003C/h3\u003E\n","\u003Cp\u003EBefore a major change, save a copy. If the AI breaks the app, you can roll back instead of trying to describe the undo. The fastest way:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecp app.py app_checkpoint.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf the next round goes wrong, restore it with:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecp app_checkpoint.py app.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you're using Git, a single commit before each round works even better: \u003Ccode\u003Egit add app.py && git commit -m "checkpoint before round 5"\u003C/code\u003E. Then \u003Ccode\u003Egit restore app.py\u003C/code\u003E to undo.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe Final App\u003C/h2\u003E\n","\u003Cp\u003EAfter 5 rounds of vibe coding, you have a data explorer with:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECSV upload with validation\u003C/li\u003E\u003Cli\u003EResponsive layout with sidebar controls\u003C/li\u003E\u003Cli\u003EData-specific metrics in bordered cards\u003C/li\u003E\u003Cli\u003EInteractive charts with column selection\u003C/li\u003E\u003Cli\u003EMaterial icons and polished column formatting\u003C/li\u003E\u003Cli\u003EEdge case handling and error messages\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHere's how each prompt built on the last:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERound\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPrompt\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Added\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Build a Streamlit data explorer app. I want to upload a CSV file and see the data in a table. Keep it simple for now."\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFile uploader, data table, basic page structure\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Move the file upload to the sidebar. Show the number of rows and columns as metrics above the table. Use wide layout."\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESidebar, \u003Ccode\u003Est.metric\u003C/code\u003E cards, wide layout\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Add a chart section below the table. Let me pick the chart type and choose which columns to use for X and Y axes. Only show numeric columns for the Y axis."\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBar/line/scatter charts, axis selectors, dtype filtering\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Add material icons to the section headers. Replace the generic row/column metrics with meaningful ones: average price, total orders, total customers, and number of products. Add conditional formatting to the data table and a download button."\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E:material/\u003C/code\u003E icons, data-specific metrics, \u003Ccode\u003Est.column_config\u003C/code\u003E, download button\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Handle these edge cases: no file uploaded yet, file has no numeric columns, file is empty, very large files over 100MB."\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFile size guard, empty-file check, friendly error messages, Getting Started placeholder\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ETotal time: ~20 minutes of prompting. No manual code written.\u003C/p\u003E\n","\u003Cp\u003ECompare this to building the same app manually: you'd need to know the Streamlit API, figure out layout patterns, handle edge cases, style the components. Easily 2-3 hours even for an experienced developer.\u003C/p\u003E\n","\u003Ch3\u003ETips for Better Results\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EUse the right AI assistant\u003C/strong\u003E: Snowflake CoCo has Streamlit agent skills built in, which means it knows current APIs and patterns. Other assistants may use outdated patterns unless you install the \u003Ca href=\"https://github.com/streamlit/agent-skills\"\u003EStreamlit Agent Skills\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKeep the conversation focused\u003C/strong\u003E: One app per conversation. Starting a new feature? Consider a new conversation if the context is getting long.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProvide sample data\u003C/strong\u003E: If your app works with specific data, show the AI a few rows. \u003Cem\u003E"The CSV has columns: date, product, price, orders, customers, region"\u003C/em\u003E is much better than \u003Cem\u003E"a CSV file."\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAsk for explanations when confused\u003C/strong\u003E: \u003Cem\u003E"Why did you use @st.cache_data here?"\u003C/em\u003E The AI explains, and you learn Streamlit along the way.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESave your best prompts\u003C/strong\u003E: When a prompt produces great results, save it. You'll reuse it for similar apps.\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built a complete Streamlit app without writing a single line of code manually. Vibe coding with Streamlit works because the framework's simplicity and visual feedback loop align perfectly with the describe-generate-see-refine workflow.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe vibe coding workflow follows four steps: \u003Cstrong\u003EDescribe\u003C/strong\u003E what you want, \u003Cstrong\u003EGenerate\u003C/strong\u003E the code with AI, \u003Cstrong\u003ESee\u003C/strong\u003E the result by running the app, and \u003Cstrong\u003ERefine\u003C/strong\u003E with follow-up prompts\u003C/li\u003E\u003Cli\u003EVibe coding is about describing intent, not dictating implementation\u003C/li\u003E\u003Cli\u003EGood prompts describe the user experience, not the code structure\u003C/li\u003E\u003Cli\u003EHandle edge cases by describing failure scenarios in plain English\u003C/li\u003E\u003Cli\u003ERunning the app after every prompt is essential for catching issues early\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ETry it with your data\u003C/strong\u003E: Replace the CSV upload with a Snowflake connection: \u003Cem\u003E"Connect to Snowflake and load data from my ORDERS table"\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDeploy it\u003C/strong\u003E: \u003Ca href=\"https://www.youtube.com/watch?v=HKoOBiAaHGg\"\u003EDeploy this app to Streamlit Community Cloud\u003C/a\u003E or \u003Ca href=\"https://www.youtube.com/watch?v=SgWxkAdjK78\"\u003EDeploy/Connect to Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExplore the agent skills\u003C/strong\u003E: Towards agentic engineering by reading this guide: \u003Ca href=\"https://www.snowflake.com/en/developers/guides/build-streamlit-apps-with-agent-skills/\"\u003EBuild Streamlit Apps with Agent Skills\u003C/a\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/streamlit/agent-skills\"\u003Estreamlit/agent-skills on GitHub\u003C/a\u003E: Agent skills for better AI-generated Streamlit code\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/\"\u003EStreamlit Documentation\u003C/a\u003E: Official docs\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ESnowflake CoCo\u003C/a\u003E: Snowflake's AI coding CLI\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Vibe Code a Streamlit App","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nVibe coding flips the traditional development loop. Instead of writing code line by line, you have a conversation like: *\"Build me a dashboard,\" \"Add a sidebar with filters,\" and \"Make the chart interactive.\"* AI then generates the code, you see the result, and you keep going until it's right.\n\nStreamlit is the ideal framework for vibe coding because:\n- **Visual feedback loop**: Save the file, see the app update instantly. You know immediately if the AI got it right.\n- **Pattern-friendly**: Most Streamlit apps follow recognizable patterns (cache data, build layout, handle state). AI assistants excel at these.\n- **Low boilerplate**: A working app can be 20 lines. This means fewer things for the AI to get wrong and faster iterations.\n- **Progressive complexity**: Start simple, add features through conversation. Each prompt builds on what's already there.\n\nThis guide walks you through building a real data explorer app from scratch, no manual coding required. By the end, you'll know which prompt patterns actually work, what to do when the AI goes sideways, and how to go from a blank file to a polished app in under 30 minutes.\n\n### What You'll Build\n\nA data explorer app that lets users upload a CSV, view key metrics, visualize data with interactive charts, and handle edge cases gracefully — all built through iterative prompting.\n\n### What You'll Learn\n\n- The vibe coding workflow: describe, generate, see, refine\n- How to write effective prompts for Streamlit development\n- When to iterate on a prompt vs. start fresh\n- How to handle edge cases and errors through conversation\n- Prompt patterns that consistently produce good Streamlit code\n\n### Prerequisites\n\n- AI coding assistant (e.g. [Snowflake CoCo](https://signup.snowflake.com/cortex-code?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides), Claude Code, Cursor, Gemini CLI, etc.) or cloud-based LLMs (e.g. ChatGPT, Claude, Gemini, etc.)\n- Python 3 installed\n- Streamlit installed (`pip install streamlit`)\n- No prior Streamlit experience needed. The AI handles the code\n\n\u003C!-- ------------------------ --\u003E\n## The Vibe Coding Workflow\n\nBefore we start building, let's understand the workflow. Vibe coding follows a simple loop:\n\n1. **Describe** what you want in plain English\n2. **Generate**: the AI writes the code\n3. **See**: run the app and look at the result\n4. **Refine**: tell the AI what to change\n\nYour role shifts from *\"programmer\"* to *\"product designer.\"* You decide what the app should do and how it should feel. The AI handles implementation. You'll still glance at the code occasionally, mostly to make sure the AI didn't do something obviously wrong, but you'll spend 90% of your time describing and evaluating, not typing.\n\n### When to Iterate vs. When to Restart\n\n- **Iterate** when the app is 70%+ correct and needs tweaks\n- **Restart** when the AI went in a fundamentally wrong direction\n- **Rule of thumb**: If your correction prompt is longer than the original prompt, consider starting over with a better initial description\n\nWhy this matters: AI assistants build on their previous output. If the foundation is wrong (wrong architecture, wrong data flow, wrong assumptions about your data), each iteration digs the hole deeper. A fresh prompt with better context is faster than 10 correction prompts fighting a bad start.\n\n### The Prompt Spectrum\n\nPrompts exist on a spectrum from vague to specific:\n\n- Too vague: *\"Make a dashboard\"* — AI guesses everything, probably wrong\n- Too specific: *\"Create a st.columns(3) with st.metric in each, using border=True, with values from df['price'].sum()\"* — You're just dictating code\n- Just right: *\"Build a dashboard with three KPI cards at the top showing revenue, orders, and customers. Add a line chart below for trends over time.\"* — Clear intent, flexible implementation\n\n\u003C!-- ------------------------ --\u003E\n## The First Prompt\n\nLet's start building. Open your AI coding assistant and type:\n\n```\nBuild a Streamlit data explorer app. I want to upload a CSV file\nand see the data in a table. Keep it simple for now.\n```\n\nThe AI generates something like this:\n\n```python\nimport streamlit as st\nimport pandas as pd\n\nst.set_page_config(page_title=\"Data Explorer\", layout=\"centered\")\nst.title(\"Data Explorer\")\n\nuploaded_file = st.file_uploader(\"Upload a CSV file\", type=[\"csv\"])\n\nif uploaded_file is not None:\n    df = pd.read_csv(uploaded_file)\n    st.dataframe(df)\nelse:\n    st.info(\"Upload a CSV file to get started.\")\n```\n\nRun it:\n\n```bash\nstreamlit run app.py\n```\n\nYou should see a file upload widget and, after uploading a CSV, a data table. It works, but it's bare-bones. That's fine. We'll build from here.\n\n### What to Look For\n\nAfter the AI generates code, do a quick sanity check:\n- Does it import streamlit?\n- Does it have `st.set_page_config()`?\n- Does it use `st.file_uploader()` with a sensible file type filter?\n- Does it handle the case where no file is uploaded yet?\n\nIf any of these are missing, mention it in your next prompt. The AI will fix it.\n\n![Round 1 app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round1.png?v=c8f3e3b8)\n\n\u003C!-- ------------------------ --\u003E\n## Improve the Layout\n\nThe app works, but it's a single column with everything stacked. Now refine the structure:\n\n```\nMove the file upload to the sidebar. Show the number of rows and\ncolumns as metrics above the table. Use wide layout.\n```\n\nThe AI restructures the app to something like this:\n\n```python\nst.set_page_config(page_title=\"Data Explorer\", layout=\"wide\")\n\nwith st.sidebar:\n    uploaded_file = st.file_uploader(\"Upload a CSV file\", type=[\"csv\"])\n\nst.title(\"Data Explorer\")\n\nif uploaded_file is not None:\n    df = pd.read_csv(uploaded_file)\n    col1, col2 = st.columns(2)\n    col1.metric(\"Rows\", df.shape[0])\n    col2.metric(\"Columns\", df.shape[1])\n    st.dataframe(df)\n```\n\nNotice how we described what we wanted, not how to code it. We said *\"metrics above the table,\"* not *\"use `st.columns(2)` with `st.metric` inside.\"* This is the core discipline of vibe coding: own the intent, delegate the implementation.\n\n![Round 2 app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round2.png?v=c8f3e3b8)\n\n\u003C!-- ------------------------ --\u003E\n## Add Charts\n\nThe layout is clean. Now make the data visual:\n\n```\nAdd a chart section below the table. Let me pick the chart type\n(bar, line, scatter) and choose which columns to use for X and Y\naxes. Only show numeric columns for the Y axis.\n```\n\nThe AI adds `st.selectbox` widgets for chart type and column selection and filters columns by dtype. It will likely reach for Plotly or Altair for the charts. That works, but Streamlit's built-in chart functions are simpler and keep the dependency list short. A quick follow-up fixes that:\n\n```\nUse Streamlit's built-in chart functions instead of Plotly.\n```\n\nNow the code uses native charts:\n\n```python\nnumeric_cols = df.select_dtypes(include=\"number\").columns.tolist()\nall_cols = df.columns.tolist()\n\nchart_type = st.selectbox(\"Chart type\", [\"bar\", \"line\", \"scatter\"])\nx_col = st.selectbox(\"X axis\", all_cols)\ny_col = st.selectbox(\"Y axis\", numeric_cols)\n\nif chart_type == \"bar\":\n    st.bar_chart(df.set_index(x_col)[y_col])\nelif chart_type == \"line\":\n    st.line_chart(df.set_index(x_col)[y_col])\nelif chart_type == \"scatter\":\n    st.scatter_chart(df, x=x_col, y=y_col)\n```\n\nThis is where vibe coding shines. Describing a feature like *\"only show numeric columns for Y axis\"* is one sentence for you but several lines of code for the AI. And when the AI picks a heavier library than you need, a single follow-up sentence is all it takes to course-correct.\n\n![Round 3 app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round3.png?v=c8f3e3b8)\n\n\u003C!-- ------------------------ --\u003E\n## Style and Polish\n\nThe app is functional. Now make it feel finished:\n\n```\nAdd material icons to the section headers. Replace the generic row/column\nmetrics with meaningful ones: average price, total orders, total customers,\nand number of products. Add conditional formatting to the data table and\na download button.\n```\n\nThe AI's first attempt will probably use emoji for icons and pull in a third-party table library. That is functional, but Streamlit has native solutions for all of these. Steer it with a follow-up:\n\n```\nUse Streamlit's :material/icon_name: syntax for icons instead of emoji.\nUse st.column_config for conditional formatting on st.dataframe.\n```\n\nIf the result still has stray dependencies or emoji shortcodes, one more nudge finishes the job:\n\n```\nRemove any third-party table dependencies. Use st.dataframe with the\nst.column_config you already defined. For icons, use the :material/icon_name:\nsyntax everywhere.\n```\n\nAfter these corrections the AI adds `:material/icon_name:` syntax to headers, replaces generic metrics with data-specific ones, adds column configuration to the dataframe, and adds `st.download_button`. Two rounds of feedback, each a few sentences, and the app now uses only built-in Streamlit features.\n\n![Round 4 app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round4.png?v=c8f3e3b8)\n\n\u003C!-- ------------------------ --\u003E\n## Handle Edge Cases\n\nThe happy path works. Now think about what could go wrong:\n\n```\nHandle these edge cases:\n- No file uploaded yet: show a friendly message with upload instructions\n- File has no numeric columns: hide the chart section and explain why\n- File is empty: show a warning\n- Very large files: show a warning if over 100MB\n```\n\nThis is one of the most underrated vibe coding patterns. You don't need to think about `if/else` logic or error handling code. Just describe what should happen when things go wrong, and the AI figures out the implementation. Plain English bullet points become guardrails.\n\n![Round 5 app](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vibe-code-a-streamlit-app/round5.png?v=c8f3e3b8)\n\n\u003C!-- ------------------------ --\u003E\n## Prompt Patterns That Work\n\nAfter building the app across 5 rounds, here are the prompt patterns that consistently produce good results:\n\n### 1. Start Broad, Then Narrow\nStart with the big picture, then zoom into details in subsequent prompts. Don't try to specify everything in prompt one.\n\n### 2. Describe the User Experience\n*\"When the user clicks X, they should see Y\"* is more effective than *\"add an on_click callback that updates session state.\"* Why: the AI knows Streamlit's event model better than most developers. Describing the desired behavior lets it pick the right mechanism (whether that's `st.session_state`, a callback, or a simple re-run) rather than locking it into an approach that might not fit.\n\n### 3. Reference What Exists\n*\"In the sidebar, below the file upload, add a multiselect for columns.\"* This tells the AI where to put things relative to what already exists.\n\n### 4. Show, Don't Tell (for Design)\n*\"Make it look like a data dashboard with KPI cards at the top, filters on the left, and charts below\"* gives the AI a concrete reference.\n\n### 5. Batch Related Changes\nInstead of 5 separate prompts for 5 small changes, combine them: *\"Fix these issues: the chart title is cut off, add a divider between sections, move the download button below the chart, add tooltips to the metrics, and change the date format to MM/DD.\"*\n\n### 6. The \"What If\" Pattern\n*\"What happens if someone uploads a file with 1 million rows? Or a file with special characters in column names?\"* This proactively surfaces edge cases.\n\n\u003C!-- ------------------------ --\u003E\n## Common Pitfalls\n\n### Over-Specifying\nTelling the AI exactly which Streamlit functions to use defeats the purpose. You're coding with extra steps. Describe intent instead.\n\n### Not Running Between Iterations\nRun the app after every prompt. Stacking 5 changes without running risks compounding errors that are hard to untangle. When something breaks after 5 unverified rounds, you won't know which prompt introduced the bug, and neither will the AI. Frequent runs give you a known-good checkpoint to describe from: *\"The chart was working after round 3, but now it's gone\"* is far more actionable than *\"something broke.\"* If the app crashes, Streamlit shows the traceback directly in the browser. Paste that exact error message into your AI assistant as your next prompt. Don't paraphrase it; the raw traceback gives the AI the context it needs to fix the problem.\n\n### Ignoring the AI's Code\nYou don't need to read every line, but do a 30-second scan after each round. Look for:\n- `st.experimental_*` functions, which are deprecated; prompt the AI: *\"Replace any deprecated `st.experimental_*` calls with their current equivalents.\"*\n- Hardcoded file paths like `/Users/yourname/...`, which break on other machines.\n- `st.cache` instead of `st.cache_data` or `st.cache_resource`. The old decorator was removed and will cause an error in current Streamlit versions.\n\n### Too Many Changes at Once\nIf a prompt asks for 10 things and the AI gets 3 wrong, you don't know which prompts worked and can't isolate what to fix. Keep rounds focused: 2-4 changes per prompt. The exception is the \"Batch Related Changes\" pattern, where grouping 5 small visual tweaks into one prompt is fine because they're independent and low-risk. The rule is about avoiding large, interdependent changes in a single round.\n\n### Not Saving Checkpoints\nBefore a major change, save a copy. If the AI breaks the app, you can roll back instead of trying to describe the undo. The fastest way:\n\n```bash\ncp app.py app_checkpoint.py\n```\n\nIf the next round goes wrong, restore it with:\n\n```bash\ncp app_checkpoint.py app.py\n```\n\nIf you're using Git, a single commit before each round works even better: `git add app.py && git commit -m \"checkpoint before round 5\"`. Then `git restore app.py` to undo.\n\n\u003C!-- ------------------------ --\u003E\n## The Final App\n\nAfter 5 rounds of vibe coding, you have a data explorer with:\n- CSV upload with validation\n- Responsive layout with sidebar controls\n- Data-specific metrics in bordered cards\n- Interactive charts with column selection\n- Material icons and polished column formatting\n- Edge case handling and error messages\n\nHere's how each prompt built on the last:\n\n| Round | Prompt | What It Added |\n|-------|--------|---------------|\n| 1 | \"Build a Streamlit data explorer app. I want to upload a CSV file and see the data in a table. Keep it simple for now.\" | File uploader, data table, basic page structure |\n| 2 | \"Move the file upload to the sidebar. Show the number of rows and columns as metrics above the table. Use wide layout.\" | Sidebar, `st.metric` cards, wide layout |\n| 3 | \"Add a chart section below the table. Let me pick the chart type and choose which columns to use for X and Y axes. Only show numeric columns for the Y axis.\" | Bar/line/scatter charts, axis selectors, dtype filtering |\n| 4 | \"Add material icons to the section headers. Replace the generic row/column metrics with meaningful ones: average price, total orders, total customers, and number of products. Add conditional formatting to the data table and a download button.\" | `:material/` icons, data-specific metrics, `st.column_config`, download button |\n| 5 | \"Handle these edge cases: no file uploaded yet, file has no numeric columns, file is empty, very large files over 100MB.\" | File size guard, empty-file check, friendly error messages, Getting Started placeholder |\n\nTotal time: ~20 minutes of prompting. No manual code written.\n\nCompare this to building the same app manually: you'd need to know the Streamlit API, figure out layout patterns, handle edge cases, style the components. Easily 2-3 hours even for an experienced developer.\n\n### Tips for Better Results\n\n1. **Use the right AI assistant**: Snowflake CoCo has Streamlit agent skills built in, which means it knows current APIs and patterns. Other assistants may use outdated patterns unless you install the [Streamlit Agent Skills](https://github.com/streamlit/agent-skills).\n2. **Keep the conversation focused**: One app per conversation. Starting a new feature? Consider a new conversation if the context is getting long.\n3. **Provide sample data**: If your app works with specific data, show the AI a few rows. *\"The CSV has columns: date, product, price, orders, customers, region\"* is much better than *\"a CSV file.\"*\n4. **Ask for explanations when confused**: *\"Why did you use @st.cache_data here?\"* The AI explains, and you learn Streamlit along the way.\n5. **Save your best prompts**: When a prompt produces great results, save it. You'll reuse it for similar apps.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You've successfully built a complete Streamlit app without writing a single line of code manually. Vibe coding with Streamlit works because the framework's simplicity and visual feedback loop align perfectly with the describe-generate-see-refine workflow.\n\n### What You Learned\n- The vibe coding workflow follows four steps: **Describe** what you want, **Generate** the code with AI, **See** the result by running the app, and **Refine** with follow-up prompts\n- Vibe coding is about describing intent, not dictating implementation\n- Good prompts describe the user experience, not the code structure\n- Handle edge cases by describing failure scenarios in plain English\n- Running the app after every prompt is essential for catching issues early\n\n### Next Steps\n1. **Try it with your data**: Replace the CSV upload with a Snowflake connection: *\"Connect to Snowflake and load data from my ORDERS table\"*\n2. **Deploy it**: [Deploy this app to Streamlit Community Cloud](https://www.youtube.com/watch?v=HKoOBiAaHGg) or [Deploy/Connect to Snowflake](https://www.youtube.com/watch?v=SgWxkAdjK78)\n3. **Explore the agent skills**: Towards agentic engineering by reading this guide: [Build Streamlit Apps with Agent Skills](https://www.snowflake.com/en/developers/guides/build-streamlit-apps-with-agent-skills/)\n\n### Related Resources\n- [streamlit/agent-skills on GitHub](https://github.com/streamlit/agent-skills): Agent skills for better AI-generated Streamlit code\n- [Streamlit Documentation](https://docs.streamlit.io/): Official docs\n- [Snowflake CoCo](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code): Snowflake's AI coding CLI\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-f02eebf0a9","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-b9f6883836",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-b4ac7e86a7","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-03-26",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-b5d547caaf","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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Vibe Coding Workflow\u003C/h2\u003E","\u003Ch2\u003EThe First Prompt\u003C/h2\u003E","\u003Ch2\u003EImprove the Layout\u003C/h2\u003E","\u003Ch2\u003EAdd Charts\u003C/h2\u003E","\u003Ch2\u003EStyle and Polish\u003C/h2\u003E","\u003Ch2\u003EHandle Edge Cases\u003C/h2\u003E","\u003Ch2\u003EPrompt Patterns That Work\u003C/h2\u003E","\u003Ch2\u003ECommon Pitfalls\u003C/h2\u003E","\u003Ch2\u003EThe Final App\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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