> ## Documentation Index > Fetch the complete documentation index at: https://docs.megallm.io/llms.txt > Use this file to discover all available pages before exploring further. # Next Steps > You've made your first AI request! Here's what to explore next. ## Learn Advanced Features Get real-time responses as they're generated Let AI interact with external tools and APIs Process images with multimodal models Complete API reference and guides ## Explore Documentation Complete API documentation OpenAI-compatible endpoints Anthropic Claude-compatible endpoints Browse all 70+ available models ## Build Real Applications ### 1. Chatbot Build an intelligent chatbot: ```python theme={null} from openai import OpenAI client = OpenAI( base_url="https://ai.megallm.io/v1", api_key="your-key" ) def chatbot(user_message, history=[]): history.append({"role": "user", "content": user_message}) response = client.chat.completions.create( model="gpt-4", messages=history ) assistant_message = response.choices[0].message.content history.append({"role": "assistant", "content": assistant_message}) return assistant_message, history ``` ### 2. Content Generator Generate blog posts, emails, or social media content: ```python theme={null} def generate_content(topic, content_type="blog"): prompts = { "blog": f"Write a comprehensive blog post about {topic}", "email": f"Write a professional email about {topic}", "tweet": f"Write an engaging tweet about {topic}" } response = client.chat.completions.create( model="claude-3.5-sonnet", messages=[{"role": "user", "content": prompts[content_type]}], temperature=0.7 ) return response.choices[0].message.content ``` ### 3. Code Assistant Build a coding helper: ```python theme={null} def code_assistant(task, language="python"): prompt = f"Write {language} code to {task}. Include comments and error handling." response = client.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": prompt}], temperature=0.2 # Lower temperature for more deterministic code ) return response.choices[0].message.content ``` ### 4. Data Analyzer Analyze data and generate insights: ```python theme={null} def analyze_data(data_description): prompt = f""" Analyze this data and provide insights: {data_description} Provide: 1. Key findings 2. Trends 3. Recommendations """ response = client.chat.completions.create( model="claude-opus-4-1-20250805", # Best for analysis messages=[{"role": "user", "content": prompt}] ) return response.choices[0].message.content ``` ## Best Practices * **GPT-4**: Best for complex reasoning * **GPT-3.5 Turbo**: Fast and cost-effective * **Claude Opus**: Excellent for analysis and long context * **Claude Sonnet**: Balanced performance * **Gemini Pro**: Strong multimodal capabilities See [Models Catalog](/home/models) for detailed comparisons. * Start with cheaper models for testing * Use `max_tokens` to limit response length * Cache responses when possible * Use streaming to improve perceived performance * Monitor usage in your dashboard ```python theme={null} from openai import OpenAI, AuthenticationError, RateLimitError import time def make_request_with_retry(messages, max_retries=3): for attempt in range(max_retries): try: return client.chat.completions.create( model="gpt-4", messages=messages ) except RateLimitError: if attempt < max_retries - 1: time.sleep(2 ** attempt) # Exponential backoff else: raise except AuthenticationError: print("Invalid API key") raise ``` * Be specific and clear * Provide examples when needed * Use system messages to set context * Break complex tasks into steps * Test different temperature settings * Keep track of conversation history * Limit history to avoid token limits * Summarize old messages if needed * Use prompt caching for repeated content ## Production Considerations ### Security * Store API keys in environment variables * Never commit keys to version control * Use different keys for dev/staging/production * Rotate keys regularly * Monitor usage for anomalies ### Performance * Use streaming for better UX * Implement caching where appropriate * Add retry logic with exponential backoff * Monitor response times * Consider using webhooks for async operations ### Monitoring * Track token usage * Monitor error rates * Log API requests (without sensitive data) * Set up alerts for quota limits * Review costs regularly ### Scaling * Implement rate limiting * Use queues for high-volume requests * Cache common responses * Consider batching requests * Plan for failover strategies ## Join the Community Chat with other developers View examples and contribute Follow for updates Watch tutorials ## Get Help Most common questions are answered in our [FAQ](/home/faq). Comprehensive guides available in [Developer Docs](/dev-docs/overview). Email us at [support@megallm.io](mailto:support@megallm.io) for technical assistance. Found a bug? Report it on [GitHub](https://github.com/megallm). ## Useful Resources * **[API Reference](/api-reference/introduction)** - Complete API documentation * **[Models Catalog](/home/models)** - All 70+ models with pricing * **[CLI Tool](/cli/overview)** - Set up AI coding assistants * **[FAQ](/home/faq)** - Common questions and answers * **[Changelog](/releases/overview)** - Latest updates ## Ready to Build? Start building your AI application today. If you need help, we're here for you! Complete documentation Get help from our team