Using LLM APIs
LLM APIs provide easy access to powerful language models without managing infrastructure. Simply send text prompts and receive AI-generated responses via HTTP requests.
Advantage: No GPUs needed! Pay per token, scale instantly, access latest models.
Popular LLM APIs
OpenAI API
GPT-4, GPT-3.5-turbo, DALL-E, Whisper.
Models: gpt-4, gpt-3.5-turbo
Pricing: ~$0.01-0.06 per 1K tokens
Features: Function calling, vision, audio
Pricing: ~$0.01-0.06 per 1K tokens
Features: Function calling, vision, audio
Anthropic API
Claude 3 (Opus, Sonnet, Haiku).
200K context window
Strong reasoning and analysis
Constitutional AI for safety
Strong reasoning and analysis
Constitutional AI for safety
Google AI (Gemini)
Gemini Pro, Gemini Ultra.
Multimodal capabilities
Free tier available
Integrated with Google Cloud
Free tier available
Integrated with Google Cloud
Basic API Usage
python
Output:
Click "Run Code" to see output
Key Parameters
temperature
Controls randomness (0-2).
0: Deterministic, focused
0.7: Balanced
1.5+: Creative, diverse
0.7: Balanced
1.5+: Creative, diverse
max_tokens
Maximum response length.
Limits output size
Affects cost
~4 chars per token
Affects cost
~4 chars per token
top_p
Nucleus sampling (0-1).
Alternative to temperature
0.9: Consider top 90% probability mass
0.9: Consider top 90% probability mass
presence_penalty
Encourage new topics (-2 to 2).
Positive: More diverse topics
Negative: Stay on topic
Negative: Stay on topic
Prompt Engineering
Be Specific
✗ Vague
"Tell me about AI"
✓ Specific
"Explain 3 key differences between supervised and unsupervised learning"
Provide Context
"You are an expert Python developer. Review this code for bugs and suggest improvements: [code]"
Use Examples (Few-Shot)
"Classify sentiment:
Text: 'I love this!' → Positive
Text: 'Terrible experience' → Negative
Text: 'It was okay' → ?"
Text: 'I love this!' → Positive
Text: 'Terrible experience' → Negative
Text: 'It was okay' → ?"
Best Practices
✓Use system messages to set behavior and context
✓Start with lower temperature for factual tasks
✓Implement rate limiting and error handling
✓Cache responses when possible to save costs
✓Monitor token usage to control expenses
✓Use streaming for better UX on long responses
✓Validate and sanitize user inputs
✓Never expose API keys in client-side code
Key Takeaway: LLM APIs make powerful AI accessible. Focus on prompt engineering, manage costs with token limits, and always handle errors gracefully.