internlm2_5-20b-chat▌

• ›API key from model provider (OpenAI, Anthropic, etc.)
• ›Programming environment (Python, Node.js, or no-code platform)
• ›Understanding of prompting techniques and best practices
• ›Budget for API costs (varies by model and usage)

30-60 minutes for basic integration, 1-2 days for production-ready implementation

• +Provide clear, specific instructions in prompts
• +Include relevant examples in few-shot prompting
• +Set max_tokens to control costs and response length
• +Use system prompts to define behavior and constraints
• +Implement retry logic with exponential backoff
• +Monitor token usage and costs per request
• +Cache responses for repeated queries
• +Validate and sanitize user inputs before sending

• −Don't trust outputs without validation—always verify facts
• −Don't send sensitive data without understanding privacy implications
• −Don't use synchronous calls in user-facing apps—implement async
• −Don't ignore error responses—handle gracefully
• −Don't use most expensive model by default—test cheaper alternatives
• −Don't skip prompt engineering—better prompts = better results

Use for content generation, summarization, Q&A, text transformation, creative writing, and any task involving understanding and generating natural language. Best for non-critical applications where occasional errors are acceptable.

Avoid for: mission-critical decisions without human oversight, medical/legal advice without expert review, real-time information (news, stock prices), exact calculations (use code instead), or when perfect factual accuracy is required.