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Route simple queries to cheap models (GPT-5.4 Mini, Gemini Flash, Haiku) and complex ones to premium models (GPT-5.4, Claude Opus). Using a lightweight classifier or a rule-based router, you can serve 80%+ of queries with cheap models and reserve expensive ones for the hard 20%.

The Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need' by Vaswani et al. at Google, replaced recurrent neural networks and became the foundation for virtually every modern large language model including GPT, Claude, Gemini, and Llama.

Llama 4 models with MoE architecture offer impressive performance for self-hosted deployments.

Use model routing: send simple tasks to cheap/fast models and complex ones to premium models.

Mixture of Experts (MoE) is an architecture where instead of one large neural network, the model consists of many smaller 'expert' networks. For any given input, a routing mechanism selects a small subset of these experts to process it. This allows models to scale up their parameter count dramatically while keeping computational costs manageable.

The 'Attention' mechanism, introduced in the paper 'Attention Is All You Need,' is the core innovation of the Transformer architecture. It allows the model to weigh the importance of different words in the input when processing text, which was a major breakthrough for handling long-range dependencies.

Most modern LLMs, including GPT and Gemini, are based on the Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need.' Its key innovation is the attention mechanism.

When asking for code, provide surrounding code snippets or describe the existing architecture. This gives the LLM context, leading to more compatible and accurate code generation.

The attention mechanism in transformers allows models to focus on relevant parts of the input when generating each token, enabling better understanding of long-range dependencies in text.