How to Write Winning Prompts for 2026’s Multimodal LLMs

The trends redefining prompt engineering in 2026

1. MoE models: more experts, fewer active parameters

Inkling 2 Small demonstrates that scalability is achieved through selective activation of experts. Prompts must guide these models to channel the appropriate computational paths. Use explicit goal statements to activate the right experts.

2. Hardware-optimized FlashAttention: Zyphra ZYFA

Zyphra’s FP8 and BF16 kernels on AMD MI355X deliver speeds up to 1.76x faster. Prompts that leverage long sequences or complex attentions benefit from a structured layout that aligns requests with prefilling capabilities.

3. Native video modeling: Gemini Omni 1.2 Flash

G Gemini Omni 1.2 Flash generates 15-second clips, supports conversational editing, and enables low-cost export. Video prompts must include temporal semantics, stylistic cues, and multi-turn editing commands.

How to build effective prompts: a step-by-step formula

Step μιά: Define the goal and output format

Start with a clear question. Specify the desired output (e.g., code, plan, video script).

Goal: Generate a 15-second video script for a technology product.
Format: Script-style subtitles with voiceover and scene directions.
Style: Professional, lively tone of voice.

Step 2: Add context and limitations

Include relevant details: target audience, aspect ratio, brand, and any time constraints.

Context: The video is for a B2B audience of IT professionals.
Limitations: No voiceover, only voice over music.

Step 3: Specify technical constraints

When working with MoE or FlashAttention models, specify sequence length, modality (text vs. image), and precision (FP8/BF16) if needed.

Technical constraints: Maximum sequence length of 512 tokens, generate only text, use FP8-compatible precision.

Step 4: Provide examples (zero-shot vs. few-shot)

An example clarifies expectations. For multimodal tasks, show the order of modalities.

Example:
User: "Create a Python code demo for a MoE model."
AI: "def mixture_of_experts(input_tensor, experts, router):
    # router selects experts
    # passes input to selected experts
    ..."

Factorial:
User: "Create a 15-second video script showcasing Inkling 2 Small's key features."
AI: [video script with scenes, captions, audio directions]

Practical examples with current 2026 tools

Prompts for Inkling 2 Small (MoE)

Use explicit goal statements to activate the right experts.

Goal: Analyze a 1M token text dataset and summarize key trends.
Instructions:
- Limit response to 5 bullet points.
- Highlight any anomalies detected.
- Use programming language for code.

Result:
1. ...
2. ...
...

Prompts for Gemini Omni 1.2 Flash (video)

Include temporal and conversational editing cues.

Generate a 15-second video showcasing Zyphra ZYFA in action.
Specifications:
- Start with a news-style introduction.
- Show FP8 inference times on AMD MI355X.
- End with caption: "Speed optimized with ZYFA".
Style: Vibrant colors, minimal voiceover.
Editing commands: Add text overlay for each speed figure.

Best practices for multimodal prompt engineering

• Be specific about modality:indicate whether you want text, image, code, or video.
• Define structure:bullet lists, tables, or scripts help the model stay on track.
• Use explicit expert markers:for MoE models, mention skills (e.g., "natural language", "numerical computation") to guide activation.
• Optimize for hardware capabilities:specify sequence lengths or FP8 precision when using optimized kernels like ZYFA.
• Engage in multi-turn feedback:for video editing tasks, ask the AI to propose changes before final export.
• Test and iterate:test different phrasings to collect examples and improve consistency.

Actionable takeaways

1. Write a markdown-style prompt for every new task: goal, context, constraints, example.
2. When using Inkling 2 Small, add a line "Activate expert: data analysis" to guide expert routing.
3. For video prompts with Gemini Omni, include temporal cues (e.g., "first second: introduction") to enhance control.
4. Save successful prompts in a personal repository and label them with model capabilities (MoE, FP8, FlashAttention) for future reference.

Conclusion: the prompt as a bridge to the future of AI

In 2026, the difference between a successful AI experiment and a failure lies in the prompt. By understanding the new capabilities of MoE models, hardware-optimized FlashAttention kernels, and native video generators, professionals can leverage these cutting-edge tools to create content, code, and multimedia experiences at unprecedented speed. Master the prompt formula, iterate with concrete feedback, and watch your results scale alongside the latest models.