AI Development ยท 15 min read
Integrating LLMs Into Your Applications
From API calls to production-ready features
Complete guide to adding AI capabilities to your apps. Covers API integration, prompt management, streaming responses, error handling, and cost optimization.
- OpenAI + alternatives
- 90% cost reduction tips
Frequently asked questions
Which LLM provider should I use for my application?
OpenAI offers the best general-purpose models, Anthropic excels at safety and long context, Google provides competitive pricing, and open-source models (Llama, Mistral) offer self-hosting options. Choose based on: use case, budget, privacy requirements, and latency needs.
How do I handle LLM API rate limits in production?
Implement exponential backoff with jitter, use request queuing, cache common responses, batch requests where possible, distribute across multiple API keys, and consider self-hosted models for high-volume use cases.
What is the cost of integrating LLMs into my application?
Costs depend on model choice, token usage, and request volume. GPT-4 costs $0.03-0.06/1K tokens, GPT-3.5 is 10-20x cheaper, Claude offers competitive rates. Estimate based on average prompt/response lengths and expected usage.
How do I reduce hallucinations in LLM outputs?
Use RAG (Retrieval Augmented Generation) to ground responses in facts, lower temperature settings, implement fact-checking pipelines, provide explicit context, and use structured output formats that constrain possible responses.