Using LLM APIs in Applications
By Karthick Selvam · Published
Most AI applications call a language model through a provider's API. Here are the essentials developers need.
The basic call
Send a list of messages (system instructions plus user input) to the API, choose a model and parameters, and receive the generated response. Responses can be streamed token by token.
Key parameters
Model choice, maximum output tokens and sampling settings such as temperature. Many APIs also support structured output and tool calling.
Production concerns
Handle rate limits and retries, set timeouts, log requests responsibly, monitor cost per request, and keep API keys in a secrets manager.
Quality
Version prompts, build an evaluation set, and test before changing models.
Frequently asked questions
Should keys be stored in code?
Never. Use environment variables or a secrets manager.
Related programmes
- Agentic AI with LangChain & LangGraph
- Building AI Agents with LangGraph & CrewAI
- Multi-Agent Systems: Design & Orchestration
Turn this into a team capability
Our corporate programmes cover topics like this hands-on, mapped to your stack.
AI & GenAI guides
Generative AI vs Traditional AI: What Is the Difference?
Traditional AI mostly predicts or classifies. Generative AI produces new content. Both have a place in the enterprise.
What Is Prompt Engineering?
Prompt engineering is the practice of writing instructions that get reliable, useful output from a language model.
What Is ChatGPT and How Does It Relate to Other AI Assistants?
ChatGPT is a conversational AI assistant from OpenAI, built on large language models. It is one of several similar assistants.