Talking to Transformers
Effective prompting of large language models relies on clear intent, strategic guidance, and understanding model types. Different models, such as reasoning and non-reasoning variants, require distinct prompting approaches for optimal performance. The article emphasizes efficiency, precision, and appropriate model selection in prompt design.
- ▪Effective prompting is built on four pillars: clear intent, guiding the model, leveraging its translation abilities, and carefully reviewing outputs.
- ▪Reasoning models like Qwen 3.6 and Gemma 4 are highlighted for their high-quality, efficient performance, with Gemma4:26bA4b now being Mira's default system model.
- ▪Non-reasoning models, such as IBM Granite 4.1, are better suited for structured tasks like JSON extraction due to their predictability and low latency.
- ▪The article advises against overloading prompts with excessive context, comparing prompt engineering for non-reasoning models to compiler design rather than natural writing.
- ▪Smaller open-source models have advanced significantly, making them viable and cost-effective alternatives to expensive proprietary models.
Opening excerpt (first ~120 words) tap to expand
2026 · PROMPTING Talking to Transformers May 2, 2026 · Taylor · 13 min read Effective prompting falls under four pillars: 1. Articulate your intent clearly using domain-specific language 2. Railroad the model into going where you want in conversation 3. Leverage the model's potential to be a universal translator of concepts and code 4. Read the outputs read the outputs holy shit just read the code the model generated But Taylor! This isn’t as fun as pasting the prompting hacks I found on Youtube for ‘best prompt chatgpt unlock creativity’. You are absolutely right. 1. Articulate your intent clearly using domain-specific language Plan the conversation before you start.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Mira.
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