01./03. (πŸ’ͺ)

πŸ’ͺ Configure the agent

The chat panel works, but the bot will happily answer anything. Ask it for the weather in Tokyo and it will tell you. Ask it about the product on the page and it has no idea which product you mean. The model also has a "reasoning" mode you can turn on so it thinks before it answers β€” and we are not using it yet.
In this exercise you will configure the agent across three dimensions:
  1. A role. A global system prompt tells the bot it is an EpicStore shopping assistant and it should only talk about shopping.
  2. The current product. When a customer asks about "this jacket", the server fetches the real product from the database using its id (sent over from the browser) and includes the product details in the system prompt.
  3. Reasoning. You turn on the model's reasoning mode with the reasoning option. The model thinks for a moment before it answers, and the chat panel shows that thinking in a collapsible block.
When you are done, the bot will politely refuse off-topic questions, answer product questions using the real product details, and show its reasoning above each answer.

🐨 What you will do

There are three small files to update. All the imports are already in place.

1. app/prompts/system.ts

A new file is set up with two empty placeholders and two Marty πŸ’° comment blocks that hold the prompt text for you. You will:
  1. 🐨 Fill in GLOBAL_SYSTEM_PROMPT with the text Marty πŸ’° gives you in the file.
  2. 🐨 Fill in buildProductPrompt(product) with the template Marty πŸ’° gives you. It uses the Product type that is already inferred from the existing getProductById helper, so you do not need to write any types.

2. app/components/product-chat.tsx

The chat panel lives in the landing layout (app/routes/_landing/_layout.tsx) so it shows up on every page β€” home, products listing, product detail, cart, and so on. Inside the panel, a small helper called useCurrentProduct() reads the product detail route's loader through useRouteLoaderData. It returns the product when the customer is on a /products/:id page, and undefined everywhere else. The product variable inside ProductChat is already wired up β€” you just need to forward it to the server.
  1. 🐨 Add forwardedProps to the useChat options. Because product can be undefined (the panel is on a page without a specific product), the value needs to be conditional: 
    forwardedProps: product
  ? { productId: product.productId }
  : {}
    When the customer is on a product detail page, the server gets the id. On any other page, no id is sent and the server falls back to the global prompt only.

3. app/routes/api.chat.tsx

The server route currently only reads messages from chatParamsFromRequest(request). You will also pull in forwardedProps, validate the new productId field, use the prompt helpers, and turn on reasoning.
  1. 🐨 Destructure forwardedProps alongside messages from await chatParamsFromRequest(request).
  2. 🐨 Validate the forwardedProps with PageForwardedProps.safeParse(...) (already imported from #app/schemas/chat.js). Return a 400 Response if validation fails. productId is optional here β€” the chat panel also shows up on the products listing page where there is no specific product.
  3. 🐨 If props.data.productId is set, call getProductById(props.data.productId) (already imported) to load the product from the database. Otherwise the product is null.
  4. 🐨 Pass systemPrompts: [GLOBAL_SYSTEM_PROMPT, buildProductPrompt(product)] into the chat() call. If the product is null (no id, or the id is unknown), pass just [GLOBAL_SYSTEM_PROMPT] instead.
  5. 🐨 Add reasoning: 'medium' to the same chat() call. This turns on the model's reasoning mode.
  6. πŸ’£ Delete the void lines at the top of the file once you are using the imports.

4. Back in app/components/product-chat.tsx

Once reasoning is on, the model sends a new kind of part called thinking along with the text parts. The parts loop already renders text; it just ignores everything else for now.
  1. 🐨 Inside the m.parts.map(...) loop, uncomment Marty πŸ’°'s thinking branch. It renders thinking parts inside a collapsible <details> so the user can expand to see the model's reasoning, right above each answer.

πŸ¦‰ About systemPrompts

chat() takes an optional systemPrompts: string[] setting. Every entry is added as a system message at the start of the conversation, before the user's first message. You can pass one big prompt or several small ones β€” the model concatenates them. Keeping the global rules separate from the per-product details makes the two layers easier to change one at a time.

πŸ¦‰ About forwardedProps

useChat lets you attach any extra JSON to every chat request through forwardedProps. On the server, those props come back as Record<string, unknown> on the result of chatParamsFromRequest(request) β€” you validate just that portion with a small Zod schema (PageForwardedProps here). We use it to pass the productId from the page, but you can pass anything you want (user id, current page, feature flags, etc.).

πŸ¦‰ About reasoning

reasoning is one option on chat() that works the same for every provider. TanStack AI turns it into the right provider field for you (for OpenRouter, that is reasoning.effort). We are using 'medium' here. The other levels are 'off', 'minimal', 'low', 'high', 'xhigh', and 'max', but a model can support only some of them. TypeScript shows which levels the current model accepts. A higher level means the model thinks for longer before it answers. You get better answers, but they are slower and use more tokens.
Provider-only settings, like temperature, topP, or seed, go in modelOptions. The fields there depend on the adapter and the model.

πŸ“œ Docs

βœ… How to know it is working

Open any product detail page and try the chat:
  • Ask "What's the weather in Tokyo?" β€” the bot says it can only help with shopping.
  • Ask "What is this product?" β€” the bot answers with the product's real name.
  • Ask "How much is it?" β€” the bot quotes the real price.
  • Open a different product detail page and ask the same question β€” the answer updates to the new product.
  • Above each answer you see a πŸ’­ Thinking… row. Click it to expand and read what the model thought about before it replied.
Then go to the products listing page (/products). The chat bubble is there too, but the header just says "Shopping Assistant" because there is no specific product. The bot still answers general shopping questions and still refuses off-topic ones β€” it just has no per-product context to lean on.
The bot's answers also come back as nicely formatted text. Bold, lists, and other markdown render through TanStack AI's TextPart component instead of showing as raw **asterisks** and - dashes.

🚨 If you get stuck

  • The bot still answers off-topic questions β†’ check that systemPrompts is set on the chat() call, and that GLOBAL_SYSTEM_PROMPT has the text from Marty πŸ’° (not an empty string).
  • The bot says it does not know the product β†’ check that the chat panel passes forwardedProps: { productId }, and that the server reads it after the PageForwardedProps.safeParse(forwardedProps) step (props.data.productId).
  • The server returns 400 β†’ chatParamsFromRequest rejected the AG-UI body, or PageForwardedProps.safeParse rejected forwardedProps. Open devtools β†’ Network and look at the request body to see which field is off.
  • getProductById returns null β†’ the productId is wrong or the product was deleted. The action should still work; it just falls back to the global prompt only.
  • You do not see a thinking row above answers β†’ check that reasoning: 'medium' is passed to chat(), and that the parts loop in product-chat.tsx handles part.type === 'thinking'. Not every model supports reasoning β€” make sure the adapter is still openRouterText('nvidia/nemotron-3-super-120b-a12b:free').
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