Sachin K Mohan

Basics

Huge thanks to Arsala Khan LinkedIn Profile 🔗 for explaining the concepts so clearly 🫶

Creating a Request

const userPrompt = "Suggest some gifts for someone who loves hiphop music";

const userMessage = {
  role: "user",
  content: userPrompt,
};

const response = await openai.chat.completions.create({
  model: process.env.AI_MODEL,
  messages: [userMessage],
});

Types of Prompts

2 Types. User Prompt and System Prompt.

System Prompt -> You are a coding expert in Javascript. You help solve problems of beginners.

User prompt -> How to split a string in JS?

Refining System Prompt to save tokens

System Prompt -> You are a coding expert in Javascript. You help solve problems of beginners. Limit your response to 100 characters.

Token Costs and Context Windows

Different models output cost differently.
Input and output tokens are charged.
Gpt-5-nano is the cheapest and gpt-5.5 is expensive
Context Window is the size of messages what fits at once. Full context is the whole chat.

Error Handling

Always add a error handling using try catch. Failing which will leave the user waiting for the response.

Structuring Responses

Take the generated Markdown response and parse it to HTML using marked npm package. Syntax marked.parse(MARKDOWN_CONTENT)
Use DOMPurify(npm package) to sanitize the html as it might contain malicious code generated by AI. It’s simple again. DOMPurify.sanitize(HTML_CONTENT)

Streaming Responses

By enabling stream true, we get output generated in chunks

const stream = await openai.chat.completions.create({
  model: process.env.AI_MODEL,
  messages,
  stream: true,
});

We can then use these chunks to render in the UI by using for await loop. for await(const chunk of stream)

Learnings from the course - Intro to AI Engineering(Scrimba) (WIP 🚧)