AI for Developers in 2026 — Building with LLM APIs

Choosing an API provider

The major LLM API providers and what they're best at:

• OpenAI — Largest ecosystem, most third-party tool support. GPT-4o for general tasks, GPT-4 for complex reasoning. Best documentation and SDK maturity.
• Anthropic (Claude) — Strongest at following complex instructions, long-context tasks (200K tokens), and careful/nuanced responses. Constitutional AI approach to safety.
• Google (Gemini) — Competitive pricing, strong multimodal capabilities (images, video, audio in same model). Tight integration with Google Cloud.
• Open-source via API — Services like Together, Fireworks, and Groq host open-source models (Llama, Mistral) with OpenAI-compatible APIs. Cheaper, faster, but less capable for complex tasks.

Practical recommendation: start with one provider's SDK, but design your code to be provider-agnostic. Most providers support OpenAI-compatible endpoints. Libraries like LiteLLM can abstract the differences. For a detailed comparison, see our AI API providers guide.

Core integration patterns

Every LLM integration falls into one of these patterns:

• Simple completion — Send text, get text back. Good for: summarisation, translation, content generation, Q&A against provided context.
• Function calling / tool use — The model decides when to call your functions and with what arguments. You execute the function and return results. Good for: database queries, API calls, calculations, any structured action.
• Embeddings + retrieval (RAG) — Convert your documents into vectors, store them, retrieve relevant chunks for each query, include them in the prompt. Good for: Q&A over your data, support bots, documentation search. See our RAG guide.
• Streaming — Receive tokens as they're generated rather than waiting for the full response. Essential for chat interfaces where users expect to see text appear progressively.
• Multi-turn conversation — Maintain a message array with role-tagged messages (system, user, assistant). The model sees the full history each turn. Manage context window limits by summarising or truncating older messages.

Production considerations

LLM APIs behave differently from traditional REST APIs. Key things to handle:

• Latency — LLM responses take 1-30 seconds depending on output length and model. Use streaming to improve perceived performance. Set appropriate timeouts (30-60s).
• Rate limits — All providers enforce rate limits (tokens per minute, requests per minute). Implement exponential backoff and request queuing.
• Cost management — Input tokens cost 3-60x less than output tokens. Keep prompts efficient. Use smaller/cheaper models for simple tasks and route complex tasks to more capable models. Cache identical requests.
• Error handling — Models can produce malformed output even with structured output modes. Always validate and parse defensively. Implement retry logic for transient API errors.
• Observability — Log every prompt, response, and token count. You can't debug AI behaviour without seeing what the model actually received and returned. Tools like LangSmith and Braintrust help.

Structured outputs and function calling

The most important pattern for production AI features is function calling (also called tool use). Instead of generating free-form text, the model outputs structured JSON that your code can act on:

1. You define available functions with their parameter schemas (JSON Schema format)
2. The model decides whether to call a function based on the user's request
3. Your code executes the function and returns the result
4. The model incorporates the result into its response

This pattern is how you build reliable features like:

• Search — model calls your search API with extracted query
• Data lookup — model queries your database with the right parameters
• Actions — model books a meeting, sends an email, creates a ticket
• Calculations — model calls your calculation functions with extracted numbers

All major providers support this: OpenAI calls it "function calling," Anthropic calls it "tool use," Google calls it "function declarations." The concept is identical.

Security and safety

AI features introduce new attack surfaces. Essential safeguards:

• Prompt injection — Users can craft inputs that override your system prompt. Never trust user input placed directly into prompts without sanitisation. Use separate system/user message roles. Validate model outputs before executing actions.
• Data leakage — Don't include sensitive data in prompts unless the user is authorised to see it. RAG systems must enforce access controls on retrieved documents.
• Output validation — Don't execute model-generated code or SQL without sandboxing. Validate all structured outputs against expected schemas.
• Cost attacks — A malicious user can craft prompts that maximise token usage. Set per-user rate limits and max_tokens caps.
• Content moderation — If your application surfaces model outputs to other users, implement moderation to catch harmful content the model's safety training might miss.