AI and LLM API Integration: Building Intelligent Features
AI and Large Language Model APIs provide sophisticated AI capabilities — text generation, summarisation, classification, data extraction, conversational interfaces, and more — via simple API calls. They enable you to build features that would otherwise require months of ML engineering, accessing state-of-the-art models through straightforward HTTP requests.
Core AI API Capabilities
- Text generation: Generate content, responses, summaries, and descriptions
- Classification: Categorise text — sentiment analysis, topic classification, content moderation
- Extraction: Extract structured data from unstructured text — parse documents, extract entities
- Embeddings: Convert text to vector representations for semantic search and similarity matching
- Function calling: AI models that can call your application functions — building agentic workflows
- Vision: Analyse images, extract text from documents, understand visual content
Major AI API Providers
- OpenAI API: GPT-4o for text, DALL-E for images, Whisper for speech, embeddings. Comprehensive and well-established.
- Anthropic (Claude): Large context window, strong reasoning and instruction-following, good for document analysis
- Google Gemini API: Strong multimodal capabilities, competitive pricing, good Google ecosystem integration
- AWS Bedrock: Managed access to multiple foundation models via unified API — Anthropic, Cohere, Meta, Amazon
Implementation Considerations
- API costs scale with token volume — monitor usage and implement caching for repeated prompts
- Latency: LLM responses take seconds — use streaming or async patterns for good UX
- Never send sensitive personal data to external AI APIs without appropriate data processing agreements
- Always validate and sanitise AI outputs before using them in your application