Complete Guide to LLM Integration

Complete Guide to LLM Integration

Large Language Models (LLMs) are transforming how we build applications. This guide covers everything you need to integrate LLMs into your products.

Understanding LLMs

LLMs like GPT-4 and Claude can:

API Integration

Most LLM providers offer REST APIs. Here's a basic example:

const response = await openai.chat.completions.create({
  model: "gpt-4",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Explain quantum computing" }
  ],
  temperature: 0.7,
  max_tokens: 500
});

RAG (Retrieval-Augmented Generation)

RAG enhances LLMs with your own data:

1. **Chunking**: Break documents into smaller pieces

2. **Embedding**: Convert text to vector representations

3. **Storage**: Store embeddings in a vector database

4. **Retrieval**: Find relevant chunks for user queries

5. **Generation**: Use LLM to generate answers from retrieved context

Best Practices

Conclusion

LLM integration opens up new possibilities for your applications. Start small, iterate based on user feedback, and scale as you learn.