Building Modern AI Applications with LLMs

Large Language Models (LLMs) have revolutionized how we build AI applications. In this post, I'll share my experience building production-ready AI applications using modern tools and frameworks.

The Foundation: Choosing the Right Stack

When building AI applications, the choice of technology stack is crucial. Here's what I typically use:

• Next.js for the frontend and API routes
• LangChain for LLM orchestration
• OpenAI's GPT-4 for natural language processing
• Vector databases for semantic search
• TypeScript for type safety

Key Considerations

1. Prompt Engineering

Good prompt engineering is crucial for reliable AI applications. Here's an example:

const systemPrompt = `You are an AI assistant that helps with code review.
Focus on:
- Code quality
- Performance
- Security
- Best practices
Be concise and specific in your feedback.`;
 
const userPrompt = `Review this code:
${codeSnippet}`;

2. Error Handling

Always implement robust error handling:

try {
  const completion = await openai.chat.completions.create({
    model: "gpt-4",
    messages: [
      { role: "system", content: systemPrompt },
      { role: "user", content: userPrompt },
    ],
    temperature: 0.7,
    max_tokens: 1500,
  });
 
  return completion.choices[0].message.content;
} catch (error) {
  console.error("OpenAI API error:", error);
  throw new Error("Failed to generate response");
}

3. Rate Limiting

Implement rate limiting to manage API costs:

import { Ratelimit } from "@upstash/ratelimit";
import { Redis } from "@upstash/redis";
 
const ratelimit = new Ratelimit({
  redis: Redis.fromEnv(),
  limiter: Ratelimit.slidingWindow(5, "1 m"),
});

Best Practices

1. Stream Responses: Use streaming for better user experience
2. Cache Results: Implement caching for common queries
3. Monitor Usage: Track API costs and usage patterns
4. Validate Inputs: Sanitize and validate all user inputs
5. Test Edge Cases: Extensively test different scenarios

Real-World Example

Here's a simple example of a chatbot implementation:

import { OpenAI } from "openai";
import { ChatCompletionMessage } from "openai/resources/chat";
 
export async function generateResponse(messages: ChatCompletionMessage[]) {
  const openai = new OpenAI();
 
  const completion = await openai.chat.completions.create({
    model: "gpt-4",
    messages,
    temperature: 0.7,
    stream: true,
  });
 
  return completion;
}

Conclusion

Building AI applications requires careful consideration of many factors. The key is to:

1. Choose the right tools
2. Implement proper error handling
3. Consider scalability from the start
4. Monitor and optimize costs
5. Continuously test and improve

Stay tuned for more posts about AI development!