Development Prompts

Prompt 1: Basic Chat UI Implementation

Date: 2025-11-01

Original Prompt:

You are working in a repo called cf_ai_askmycampus, created from the Cloudflare vite-react template.

Task:

Create a basic chat UI in React:
- A chat history area that shows messages in order.
- An input box and a Send button.
- Local state for
```
messages
```
  (array of { role: "user" | "assistant", content: string }).
- On Send:
  - Append the user's message to local state immediately.
  - POST to
```
/api/chat
```
    with { sessionId, message } as JSON.
  - Read the JSON response
```
{ reply }
```
    and append that as
```
{ role: "assistant", content: reply }
```
    .
Generate or reuse a
```
sessionId
```
and store it in
```
localStorage
```
so it persists across page reloads:
- On first load, if no sessionId exists, create a random UUID and store it.
- Include
```
sessionId
```
  in the POST body.
Add very simple styling (just flex/column, scrollable history div, nothing fancy).
Update the main App component (or create a Chat component and render it from App) so that when I run the dev server I see the chat box.
Also, append THIS EXACT prompt text and a short summary of what code you generated to a file called
```
PROMPTS.md
```
at the repo root. If
```
PROMPTS.md
```
doesn't exist yet, create it.

Notes:

Don't wire up the backend call logic yet beyond the fetch(
```
/api/chat
```
). The /api/chat Worker doesn't exist yet – that's the next step.
Keep the code clean and readable, TypeScript if the template is TS, JS if the template is JS.
Do not add any external component libraries.

Goal:

I want a minimal working chat UI that can talk to

/api/chat

once I implement the backend.

Summary of Generated Code:

Created a fully functional chat UI with the following components:

Chat.tsx - Main chat component with:
- TypeScript interface for Message type (user/assistant roles)
- UUID generation function for sessionId
- localStorage integration for persistent sessionId across page reloads
- Local state management for messages array
- Async fetch call to
```
/api/chat
```
  endpoint with sessionId and message in POST body
- Error handling for failed API calls
- Auto-scrolling to bottom on new messages
- Loading state during API calls
- Enter key support for sending messages
Chat.css - Simple, clean styling with:
- Flexbox column layout for full-height chat container
- Scrollable message history area
- Distinct styling for user vs assistant messages (blue for user, gray for assistant)
- Responsive input area with Send button
- Empty state message when no chat history exists
App.tsx - Updated to render the Chat component instead of boilerplate content
index.css - Modified body and #root styles to support full-height chat layout

The implementation uses no external libraries, is fully TypeScript-typed, and ready to connect to the backend

/api/chat

endpoint once implemented.

Prompt 2: Worker API Chat Endpoint Implementation

Date: 2025-11-01

Original Prompt:

You are editing the cf_ai_askmycampus project created from the Cloudflare vite-react template.

Goal:

Implement a Worker route

/api/chat

that will act as the coordination layer for the AI assistant.

Requirements:

Create (or update) the Worker entrypoint so it handles a POST request to
```
/api/chat
```
.
- Parse JSON from the request body:
```
{ sessionId, message }
```
  .
- If either is missing, return 400.
Conversation memory:
- We'll be using a KV namespace for memory. For now:
  - Assume we have a binding called
```
CHAT_HISTORY
```
    (Cloudflare KV).
  - Key is the
```
sessionId
```
    .
  - Value is a JSON string of an array of messages shaped like:
```
{ role: "user" | "assistant", content: string }[]
```
    .
- On each request:
  - Get the history from KV (if missing, default to
```
[]
```
    ).
  - Append the new user message to the history.
  - Build a prompt string for the LLM from that history. For now just join messages like:
    
    "user: ...\nassistant: ...\nuser: ...\n"
  - Call a placeholder
```
generateAssistantReply(prompt)
```
    function (see #3).
  - Append the assistant reply to history.
  - Save the updated history back to KV under that same
```
sessionId
```
    .
Stub the LLM call:
- Implement
```
async function generateAssistantReply(prompt: string, env: Env): Promise<string>
```
  .
- For now, DO NOT call a real model. Just return something like:
```
"This is a placeholder AI response based on: " + prompt.slice(0, 200)
```
- We will later replace this with a real Workers AI (Llama 3.3) call.
Response:
- Return JSON
```
{ reply: <assistantReply> }
```
  so the frontend can render it.
Types / bindings:
- Define an
```
Env
```
  interface that includes:
  - ```
  CHAT_HISTORY: KVNamespace
```
  (for KV)
- Make sure the fetch handler is
```
export default { fetch(request, env) { ... } }
```
  or whatever pattern the template uses.
- Use TypeScript if the Worker is TS, JS if the Worker is JS. Match the template.
Add/update
```
wrangler.toml
```
:
- Add a KV binding stub so we remember to wire it up for real deploy:
  
  [[kv_namespaces]]
  
  binding = "CHAT_HISTORY"
  
  id = "CHAT_HISTORY_DEV"
- If the template already has wrangler.toml, modify it instead of creating a duplicate. If something similar already exists, extend it.
- If wrangler.toml is managed differently in this template, follow that structure.
Make sure CORS / headers are fine for local dev:
- Return a Response with
```
content-type: application/json
```
  .
- You can assume same-origin fetch from the React dev server for now, so no need to overcomplicate CORS.
IMPORTANT FOR THE ASSIGNMENT:
- Append THIS EXACT prompt and a short summary of the code you generated to
```
PROMPTS.md
```
  at the repo root. If
```
PROMPTS.md
```
  exists, append. If it doesn't, create it.
- The summary in PROMPTS.md should mention:
  - Added /api/chat route
  - KV-based memory per sessionId
  - placeholder LLM function
  - updated wrangler.toml with KV binding

After this change, I should be able to:

run wrangler dev
send a POST to /api/chat with { sessionId, message }
get back { reply: "placeholder..." }
and see that memory is persisted per session in KV.

Summary of Generated Code:

Implemented the

/api/chat

Worker endpoint with full conversation memory using Cloudflare KV:

worker/index.ts - Updated Worker with:
- Added
```
Env
```
  interface with
```
CHAT_HISTORY: KVNamespace
```
  binding
- Added
```
Message
```
  interface for type safety (role: "user" | "assistant", content: string)
- Implemented POST
```
/api/chat
```
  route that:
  - Validates required fields (sessionId, message) and returns 400 if missing
  - Retrieves conversation history from KV by sessionId (defaults to empty array if new)
  - Appends user message to history
  - Builds a formatted prompt string from conversation history
  - Calls placeholder
```
generateAssistantReply()
```
    function
  - Appends assistant reply to history
  - Saves updated history back to KV
  - Returns JSON response
```
{ reply: assistantReply }
```
- Created
```
generateAssistantReply()
```
  placeholder function that returns mock response with prompt preview
- Created
```
buildPrompt()
```
  helper function to format conversation history as "role: content" lines
- Added error handling with try/catch and proper error responses
wrangler.json - Added KV namespace binding:
- Added
```
kv_namespaces
```
  array with CHAT_HISTORY binding
- Set binding name as "CHAT_HISTORY" and id as "CHAT_HISTORY_DEV" for development
- Added documentation comments for the KV configuration

The implementation:

Uses Hono framework (already in template) with proper TypeScript typing
Returns JSON responses with correct content-type headers (handled by Hono's c.json())
Maintains conversation context per sessionId in KV
Ready for local testing with
```
wrangler dev
```
or
```
npm run dev
```
Placeholder LLM function is ready to be replaced with real Workers AI (Llama 3.3) call in the next step

Prompt 3: README Documentation

Date: 2025-11-01

Original Prompt:

Create a concise README.md for this repo (

cf_ai_askmycampus

Include:

A short intro explaining this is an AI-powered chat assistant built on Cloudflare (Workers AI + KV + Pages) for the Cloudflare internship assignment.
A section explaining how it meets the 4 requirements (LLM, workflow, user input via chat, memory).

A "Run Locally" section with exact commands:

npm install -g wrangler
wrangler login
npm install
npx wrangler dev (needs npm run build)

Development Prompts

Prompt 1: Basic Chat UI Implementation

Date: 2025-11-01

Original Prompt:

You are working in a repo called cf_ai_askmycampus, created from the Cloudflare vite-react template.

Task:

Create a basic chat UI in React:
- A chat history area that shows messages in order.
- An input box and a Send button.
- Local state for
```
messages
```
  (array of { role: "user" | "assistant", content: string }).
- On Send:
  - Append the user's message to local state immediately.
  - POST to
```
/api/chat
```
    with { sessionId, message } as JSON.
  - Read the JSON response
```
{ reply }
```
    and append that as
```
{ role: "assistant", content: reply }
```
    .
Generate or reuse a
```
sessionId
```
and store it in
```
localStorage
```
so it persists across page reloads:
- On first load, if no sessionId exists, create a random UUID and store it.
- Include
```
sessionId
```
  in the POST body.
Add very simple styling (just flex/column, scrollable history div, nothing fancy).
Update the main App component (or create a Chat component and render it from App) so that when I run the dev server I see the chat box.
Also, append THIS EXACT prompt text and a short summary of what code you generated to a file called
```
PROMPTS.md
```
at the repo root. If
```
PROMPTS.md
```
doesn't exist yet, create it.

Notes:

Don't wire up the backend call logic yet beyond the fetch(
```
/api/chat
```
). The /api/chat Worker doesn't exist yet – that's the next step.
Keep the code clean and readable, TypeScript if the template is TS, JS if the template is JS.
Do not add any external component libraries.

Goal:

I want a minimal working chat UI that can talk to

/api/chat

once I implement the backend.

Summary of Generated Code:

Created a fully functional chat UI with the following components:

Chat.tsx - Main chat component with:
- TypeScript interface for Message type (user/assistant roles)
- UUID generation function for sessionId
- localStorage integration for persistent sessionId across page reloads
- Local state management for messages array
- Async fetch call to
```
/api/chat
```
  endpoint with sessionId and message in POST body
- Error handling for failed API calls
- Auto-scrolling to bottom on new messages
- Loading state during API calls
- Enter key support for sending messages
Chat.css - Simple, clean styling with:
- Flexbox column layout for full-height chat container
- Scrollable message history area
- Distinct styling for user vs assistant messages (blue for user, gray for assistant)
- Responsive input area with Send button
- Empty state message when no chat history exists
App.tsx - Updated to render the Chat component instead of boilerplate content
index.css - Modified body and #root styles to support full-height chat layout

The implementation uses no external libraries, is fully TypeScript-typed, and ready to connect to the backend

/api/chat

endpoint once implemented.

Prompt 2: Worker API Chat Endpoint Implementation

Date: 2025-11-01

Original Prompt:

You are editing the cf_ai_askmycampus project created from the Cloudflare vite-react template.

Goal:

Implement a Worker route

/api/chat

that will act as the coordination layer for the AI assistant.

Requirements:

Create (or update) the Worker entrypoint so it handles a POST request to
```
/api/chat
```
.
- Parse JSON from the request body:
```
{ sessionId, message }
```
  .
- If either is missing, return 400.
Conversation memory:
- We'll be using a KV namespace for memory. For now:
  - Assume we have a binding called
```
CHAT_HISTORY
```
    (Cloudflare KV).
  - Key is the
```
sessionId
```
    .
  - Value is a JSON string of an array of messages shaped like:
```
{ role: "user" | "assistant", content: string }[]
```
    .
- On each request:
  - Get the history from KV (if missing, default to
```
[]
```
    ).
  - Append the new user message to the history.
  - Build a prompt string for the LLM from that history. For now just join messages like:
    
    "user: ...\nassistant: ...\nuser: ...\n"
  - Call a placeholder
```
generateAssistantReply(prompt)
```
    function (see #3).
  - Append the assistant reply to history.
  - Save the updated history back to KV under that same
```
sessionId
```
    .
Stub the LLM call:
- Implement
```
async function generateAssistantReply(prompt: string, env: Env): Promise<string>
```
  .
- For now, DO NOT call a real model. Just return something like:
```
"This is a placeholder AI response based on: " + prompt.slice(0, 200)
```
- We will later replace this with a real Workers AI (Llama 3.3) call.
Response:
- Return JSON
```
{ reply: <assistantReply> }
```
  so the frontend can render it.
Types / bindings:
- Define an
```
Env
```
  interface that includes:
  - ```
  CHAT_HISTORY: KVNamespace
```
  (for KV)
- Make sure the fetch handler is
```
export default { fetch(request, env) { ... } }
```
  or whatever pattern the template uses.
- Use TypeScript if the Worker is TS, JS if the Worker is JS. Match the template.
Add/update
```
wrangler.toml
```
:
- Add a KV binding stub so we remember to wire it up for real deploy:
  
  [[kv_namespaces]]
  
  binding = "CHAT_HISTORY"
  
  id = "CHAT_HISTORY_DEV"
- If the template already has wrangler.toml, modify it instead of creating a duplicate. If something similar already exists, extend it.
- If wrangler.toml is managed differently in this template, follow that structure.
Make sure CORS / headers are fine for local dev:
- Return a Response with
```
content-type: application/json
```
  .
- You can assume same-origin fetch from the React dev server for now, so no need to overcomplicate CORS.
IMPORTANT FOR THE ASSIGNMENT:
- Append THIS EXACT prompt and a short summary of the code you generated to
```
PROMPTS.md
```
  at the repo root. If
```
PROMPTS.md
```
  exists, append. If it doesn't, create it.
- The summary in PROMPTS.md should mention:
  - Added /api/chat route
  - KV-based memory per sessionId
  - placeholder LLM function
  - updated wrangler.toml with KV binding

After this change, I should be able to:

run wrangler dev
send a POST to /api/chat with { sessionId, message }
get back { reply: "placeholder..." }
and see that memory is persisted per session in KV.

Summary of Generated Code:

Implemented the

/api/chat

Worker endpoint with full conversation memory using Cloudflare KV:

worker/index.ts - Updated Worker with:
- Added
```
Env
```
  interface with
```
CHAT_HISTORY: KVNamespace
```
  binding
- Added
```
Message
```
  interface for type safety (role: "user" | "assistant", content: string)
- Implemented POST
```
/api/chat
```
  route that:
  - Validates required fields (sessionId, message) and returns 400 if missing
  - Retrieves conversation history from KV by sessionId (defaults to empty array if new)
  - Appends user message to history
  - Builds a formatted prompt string from conversation history
  - Calls placeholder
```
generateAssistantReply()
```
    function
  - Appends assistant reply to history
  - Saves updated history back to KV
  - Returns JSON response
```
{ reply: assistantReply }
```
- Created
```
generateAssistantReply()
```
  placeholder function that returns mock response with prompt preview
- Created
```
buildPrompt()
```
  helper function to format conversation history as "role: content" lines
- Added error handling with try/catch and proper error responses
wrangler.json - Added KV namespace binding:
- Added
```
kv_namespaces
```
  array with CHAT_HISTORY binding
- Set binding name as "CHAT_HISTORY" and id as "CHAT_HISTORY_DEV" for development
- Added documentation comments for the KV configuration

The implementation:

Uses Hono framework (already in template) with proper TypeScript typing
Returns JSON responses with correct content-type headers (handled by Hono's c.json())
Maintains conversation context per sessionId in KV
Ready for local testing with
```
wrangler dev
```
or
```
npm run dev
```
Placeholder LLM function is ready to be replaced with real Workers AI (Llama 3.3) call in the next step

Prompt 3: README Documentation

Date: 2025-11-01

Original Prompt:

Create a concise README.md for this repo (

cf_ai_askmycampus

Include:

A short intro explaining this is an AI-powered chat assistant built on Cloudflare (Workers AI + KV + Pages) for the Cloudflare internship assignment.
A section explaining how it meets the 4 requirements (LLM, workflow, user input via chat, memory).

A "Run Locally" section with exact commands:

npm install -g wrangler
wrangler login
npm install
npx wrangler dev (needs npm run build)

Development Prompts

Development Prompts

Prompt 1: Basic Chat UI Implementation

Original Prompt:

Summary of Generated Code:

Prompt 2: Worker API Chat Endpoint Implementation

Original Prompt:

Summary of Generated Code:

Prompt 3: README Documentation

Original Prompt:

Related Skills

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Development Prompts

Prompt 1: Basic Chat UI Implementation

Original Prompt:

Summary of Generated Code:

Prompt 2: Worker API Chat Endpoint Implementation

Original Prompt:

Summary of Generated Code:

Prompt 3: README Documentation

Original Prompt: