[{"data":1,"prerenderedAt":386},["ShallowReactive",2],{"docs-\u002Fdocs\u002Fai-chat\u002Fexamples":3},{"id":4,"title":5,"body":6,"description":377,"extension":378,"meta":379,"navigation":380,"path":381,"redirect":382,"seo":383,"stem":384,"__hash__":385},"docs\u002Fdocs\u002Fai-chat\u002Fexamples.md","Examples",{"type":7,"value":8,"toc":368},"minimark",[9,13,17,27,32,45,76,80,83,90,148,152,159,212,219,230,238,244,248,255,273,277,284,303,307,310,325,338,342,364],[10,11,5],"h1",{"id":12},"examples",[14,15,16],"p",{},"Every exchange below is a real prompt and a real answer from a live workspace —\nnumbers, table names, and all. Yours will differ; the shape won't.",[18,19,20],"blockquote",{},[14,21,22,26],{},[23,24,25],"strong",{},"You do not need a top-tier model for these everyday tasks."," We tested these\nworkflows with a smaller, lower-cost model and got useful results. A stronger\nmodel may help with an unusually ambiguous or complex request, but it is not a\nrequirement for exploring a schema, asking for numbers, or checking a stream.",[28,29,31],"h2",{"id":30},"inspect-a-schema","Inspect a schema",[18,33,34],{},[14,35,36,39,40,44],{},[23,37,38],{},"You:"," Describe the ",[41,42,43],"code",{},"orders"," table on my Postgres demo connection — columns,\nkeys, indexes. Lay it out as a table.",[18,46,47],{},[14,48,49,52,53,56,57,60,61,64,65,67,68,71,72,75],{},[23,50,51],{},"AI:"," ",[41,54,55],{},"public.orders"," — 10 columns, primary key ",[41,58,59],{},"order_id",", foreign key\n",[41,62,63],{},"customer_id → customers",". Indexes on ",[41,66,59],{}," (PK) and ",[41,69,70],{},"customer_id",". No\nindex on ",[41,73,74],{},"region"," — add one if you group orders by region.",[28,77,79],{"id":78},"ask-for-real-numbers","Ask for real numbers",[14,81,82],{},"The assistant runs the query — it doesn't guess.",[18,84,85],{},[14,86,87,89],{},[23,88,38],{}," What are my top film categories by rental revenue? (sakila sample)",[18,91,92,96,141],{},[14,93,94],{},[23,95,51],{},[97,98,99,113],"table",{},[100,101,102],"thead",{},[103,104,105,109],"tr",{},[106,107,108],"th",{},"Category",[106,110,112],{"align":111},"right","Revenue",[114,115,116,125,133],"tbody",{},[103,117,118,122],{},[119,120,121],"td",{},"Sports",[119,123,124],{"align":111},"$5,314.21",[103,126,127,130],{},[119,128,129],{},"Sci-Fi",[119,131,132],{"align":111},"$4,756.98",[103,134,135,138],{},[119,136,137],{},"Animation",[119,139,140],{"align":111},"$4,656.30",[14,142,143,144,147],{},"Joined ",[41,145,146],{},"category → film_category → film → inventory → rental → payment",", ran in\n~55 ms. Want the query to reuse?",[28,149,151],{"id":150},"optimize-a-query","Optimize a query",[14,153,154,155,158],{},"Open ",[23,156,157],{},"Ask AI"," from a SQL Console to attach the query you are actually working\non, then ask what to improve. For example:",[160,161,166],"pre",{"className":162,"code":163,"language":164,"meta":165,"style":165},"language-sql shiki shiki-themes github-light github-dark","SELECT c.customer_id, c.first_name, c.last_name, COUNT(r.rental_id) AS rentals\nFROM customer AS c\nJOIN rental AS r ON r.customer_id = c.customer_id\nWHERE r.rental_date >= '2005-06-01'\nGROUP BY c.customer_id, c.first_name, c.last_name\nORDER BY rentals DESC\nLIMIT 20;\n","sql","",[41,167,168,176,182,188,194,200,206],{"__ignoreMap":165},[169,170,173],"span",{"class":171,"line":172},"line",1,[169,174,175],{},"SELECT c.customer_id, c.first_name, c.last_name, COUNT(r.rental_id) AS rentals\n",[169,177,179],{"class":171,"line":178},2,[169,180,181],{},"FROM customer AS c\n",[169,183,185],{"class":171,"line":184},3,[169,186,187],{},"JOIN rental AS r ON r.customer_id = c.customer_id\n",[169,189,191],{"class":171,"line":190},4,[169,192,193],{},"WHERE r.rental_date >= '2005-06-01'\n",[169,195,197],{"class":171,"line":196},5,[169,198,199],{},"GROUP BY c.customer_id, c.first_name, c.last_name\n",[169,201,203],{"class":171,"line":202},6,[169,204,205],{},"ORDER BY rentals DESC\n",[169,207,209],{"class":171,"line":208},7,[169,210,211],{},"LIMIT 20;\n",[18,213,214],{},[14,215,216,218],{},[23,217,38],{}," Explain this query and suggest the safest improvement.",[18,220,221],{},[14,222,223,225,226,229],{},[23,224,51],{}," The date filter is the expensive part. Check the plan first; if it\nscans too many rentals, test an index beginning with ",[41,227,228],{},"rental_date",".",[231,232],"img",{"className":233,"src":236,"alt":237},[234,235],"block","dark:hidden","\u002Fdocs\u002Fimages\u002Fgenerated\u002Fai-chat-query-analysis-light.png","AI Chat analysing a query opened from the SQL Console",[231,239],{"className":240,"src":243,"alt":237},[241,242],"hidden","dark:block","\u002Fdocs\u002Fimages\u002Fgenerated\u002Fai-chat-query-analysis-dark.png",[28,245,247],{"id":246},"troubleshoot-a-stream","Troubleshoot a stream",[18,249,250],{},[14,251,252,254],{},[23,253,38],{}," My MySQL → PostgreSQL CDC stream stopped overnight. List my streams,\ncheck status and recent errors, and tell me what to fix.",[18,256,257],{},[14,258,259,261,262,52,265,268,269,272],{},[23,260,51],{}," Found it — the latest run of ",[41,263,264],{},"10M-cdc-mysql2pg",[23,266,267],{},"FAILED"," with\n",[41,270,271],{},"Unknown database 'target'",", source-side. The CDC source points at a database\nthat doesn't exist on the MySQL server. Fix: create it (or correct the name on\nthe connection), then restart.",[28,274,276],{"id":275},"summarize-what-your-streams-do","Summarize what your streams do",[18,278,279],{},[14,280,281,283],{},[23,282,38],{}," Summarize what my streams migrate.",[18,285,286],{},[14,287,288,52,290,294,295,298,299,302],{},[23,289,51],{},[291,292,293],"em",{},"(a table of every config)"," 6 one-shot ",[23,296,297],{},"loads"," and 1 ",[23,300,301],{},"CDC"," stream —\na full Sakila dataset replicated across MySQL, PostgreSQL, and CSV. The CDC\nstream to PostgreSQL is incomplete (stopped mid-run, 3 of 16 tables synced).",[28,304,306],{"id":305},"validate-across-sources","Validate across sources",[14,308,309],{},"In a SQL console with two or more sources attached — say a MySQL database and a\nfolder of CSVs — AI Chat sees every source and its alias, and writes the\ncross-source query for you.",[18,311,312],{},[14,313,314,316,317,320,321,324],{},[23,315,38],{}," Is my ",[41,318,319],{},"customers.csv"," export still in sync with the live ",[41,322,323],{},"customers","\ntable? Compare row counts and find rows only in one side.",[14,326,327,328,333,334,229],{},"Full multi-source transcripts:\n",[329,330,332],"a",{"href":331},"\u002Fdocs\u002Fmcp\u002Fexamples\u002Fsql-exploration","plain-English SQL exploration"," ·\n",[329,335,337],{"href":336},"\u002Fdocs\u002Fmcp\u002Fexamples\u002Ffederated-validation","Postgres + Parquet validation",[28,339,341],{"id":340},"next","Next",[343,344,345,357],"ul",{},[346,347,348,352,353],"li",{},[329,349,351],{"href":350},"\u002Fdocs\u002Fai-chat\u002Fusing-ai-chat","Using AI Chat"," · ",[329,354,356],{"href":355},"\u002Fdocs\u002Fai-chat\u002Fgenerate-sql","Generate SQL",[346,358,359,363],{},[329,360,362],{"href":361},"\u002Fdocs\u002Fmcp\u002Ftools","Tools reference"," — every read-only tool behind these answers.",[365,366,367],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":165,"searchDepth":178,"depth":178,"links":369},[370,371,372,373,374,375,376],{"id":30,"depth":178,"text":31},{"id":78,"depth":178,"text":79},{"id":150,"depth":178,"text":151},{"id":246,"depth":178,"text":247},{"id":275,"depth":178,"text":276},{"id":305,"depth":178,"text":306},{"id":340,"depth":178,"text":341},"Real end-to-end exchanges with AI Chat — schema inspection, analytics, query optimization, stream troubleshooting, and cross-source validation.","md",{},false,"\u002Fdocs\u002Fai-chat\u002Fexamples",null,{"title":5,"description":377},"docs\u002Fai-chat\u002Fexamples","_wPriZYimUkBZLDvf-ShYvdhOZ49hgRDvqWoiS5N1Rw",1784670669678]