Introduction: The challenge of prompt engineering in 2026
In 2026, language models are evolving faster than ever. With the arrival of multimodal models with 120 billion parameters such as Nemotron 3.5 Super VL, Meta’s 816B MoE called Muse Spark Open, and Liquid AI’s new decision model d1-ultra, old prompt strategies are no longer sufficient. This article explains how to apply advanced prompt engineering to get the most out of these cutting-edge technologies.
Why old prompts are no longer enough
Next-generation models offer extended contexts of up to 1 million tokens, multimodal capabilities, and MoE architectures that change how they process information. To obtain precise results, a prompt must:
- Be explicit about the desired format (JSON, markdown, code)
- Define the AI’s role and expertise clearly
- Leverage the extended context to provide chronological or spatial references
Ignoring these aspects leads to generic, incomplete, or even incorrect answers.
New model capabilities
Before diving into techniques, it’s useful to know the key features of the latest models:
- Context window:
- Multimodality:simultaneous processing of text, images, and audio.
- MoE (Mixture of Experts):only a portion of the parameters is active per token, improving performance and reducing energy consumption.
- Calibrated output:models like d1-ultra return reliable probabilities in a single forward pass.
Understanding these capabilities helps you write prompts that leverage them fully.
Advanced prompt engineering techniques
1. Explicit, role-based prompts
Defining the AI’s role eliminates ambiguity and improves consistency.
system: You are an expert data analyst with experience in Python and pandas. Provide clean code, comments, and brief explanations.
user: Load the sales_2026.csv file, filter the data by region, and calculate the total monthly revenue for each area. Return the result in a JSON block.
assistant: ...2. Chain-of-Thought (CoT) for complex problems
Asking the