APIs are the connective tissue of AI development — they let your application communicate with AI models and services without building or hosting those models yourself.
Consider: task type (coding, reasoning, vision), context window size, latency requirements, cost per token, data privacy (cloud vs self-hosted), and model licensing. Open-source models (Llama, Mistral, Qwen) are preferable when data must stay on-premise. Benchmark on your specific workload leaderboard scores rarely translate directly.
Tokens are the units models process roughly 4 characters or three-quarters of a word in English. API pricing is per-token (input + output). Longer prompts consume more tokens and increase latency. Always profile token usage in production; use streaming to improve perceived latency for long outputs.
The context window is the maximum number of tokens a model can ‘see’ at once (prompt + response). Larger windows let you include more documents or conversation history, but also increase cost and can dilute attention on key information (‘lost in the middle’ problem). Chunk and retrieve rather than stuffing everything in.
Fine-tuning continues training a pre-trained model on your own dataset to adapt its style, format, or domain knowledge. Use it when prompt engineering alone can’t achieve the required output format consistency, or when you need to bake in proprietary domain knowledge efficiently. It requires quality labelled data and is more expensive than prompting.
Zero-shot: no examples in the prompt, relying on the model’s pre-trained knowledge. Few-shot: include 2-10 input/output examples in the prompt to guide format/style. Fine-tuning: actually update model weights on hundreds to thousands of examples. Start with zero-shot, then few-shot, then fine-tuning in that order; each step is more costly.
