Introduction: Why today’s prompts need to evolve
1. Understanding the new frontier of LLMs
1.1 Multimodal models and the Open Dreamer case study
Open Dreamer, released in July 2026, is an open-source recreation of the Dreamer 4 world model, built using JAX/Flax. Unlike traditional text-based LLMs, Dreamer 4 generates world representations, simulates interactions, and produces code, images, and action descriptions in a consistent manner.
Learn from Open Dreamer:
- The most effective prompts explicitly define the desiredworld model(e.g., physics, economics, social dynamics).
- Incorporate aclosed-loop structure: "Observe state X, decide action Y, obtain result Z."
- Employschema-based prompt engineeringto guide the generation of reusable code.
1.2 Security pipelines: Sakana AI’s approach with Fugu-Cyber
Fugu-Cyber, developed by Sakana AI, achieved scores of 86.9% on CyberGym and 72.1% on CTI-REALM. This orchestration model specializes in threat classification and incident response. Its internal prompts clearly distinguish between:
- Detection:"Identify indicators of compromise in this system log."
- Assessment:"Evaluate severity based on impact and spread."
- Action:"Generate a detailed containment playbook."
When designing prompts for security pipelines, maintaining this three-part structure ensures reliable outcomes.
2. Prompt engineering for today’s tasks in 2026
2.1 Code generation with multimodal models
If you’re using an LLM capable of generating both code and visual comments, an effective prompt might be:
Generate a Python snippet that implements a Transformer model for text classification. Include docstrings, comments, and a brief visual explanation of the attention mask diagram.This prompt works because:
- It specifies aspecific output(Python code with docstrings).
- It requests avisual explanation, leveraging the model’s multimodal capabilities.
- It usesclear and concrete languageto minimize ambiguity.
2.2 Creating training scenarios for robotics
To train a reinforcement learning agent with realistic world scenarios, try this prompt inspired by Open Dreamer:
Create a simulated scenario where a mobile robot must navigate a dynamic urban environment. Define:
- Initial maps (10x10 grid)
- Obstacles (pedestrians, vehicles, traffic lights)
- Goal reward (reach the drop point)
- Transition function (realistic physics)
Provide pseudo-code for the state initializer and reward logic.This prompt breaks the task into manageable parts and provides the model with a concrete template.
2.3 Prompts for security orchestration
When using a model like Fugu-Cyber for incident response, separate the analysis phase from the remediation phase:
Provide a detailed incident analysis for the following network log:
[paste log]Next, based on the analysis, generate a step-by-step containment playbook.
This separation keeps the prompt focused and enhances accuracy.
3. Advanced prompt engineering techniques in 2026
- Chain-of-Thought (CoT) with structured constraints:Use "Steps:" before the response to enforce explicit reasoning.
- Few-shot prompts:
- Adaptive prompts:Start with a generic prompt, then refine it based on user feedback (using iterative loops).
- Multimodal context:Include references to images, code, or log files directly in the prompt (if the model supports it).
These techniques are particularly effective when working with cutting-edge models like those used by AMD and Anthropic, where hardware and software are optimized for high-speed inference.
4. Practical example: an end-to-end workflow
Suppose you need to create an automated security report for a company using Fugu-Cyber orchestration.
Extract all relevant network events from the last 24 hours of logs and provide a tabular summary: timestamp, source, destination, severity.Based on the table above, identify potential security policy violations. List each violation, its likely cause, and estimated risk (low/medium/high).For each violation listed, generate a detailed containment playbook that includes: immediate steps, forensic actions, and recommended configuration changes.By combining these three prompts into a single workflow, you create an automated, precise, and ready-to-use incident response system.
Conclusion: Your competitive advantage in 2026
Today’s AI models are not just about completing a sentence. They are platforms capable of generating code, simulating worlds, and safeguarding critical infrastructure. Crafting effective prompts requires understanding the model’s capabilities, structuring tasks clearly, and applying proven techniques like CoT, few-shot learning, and multimodal constraints.
Whether you’re developing a robotic agent, writing code, or building a security system, a well-designed prompt is the first step toward reliable, high-quality results.
Final takeaways
- Study the model:Examine Open Dreamer’s code to understand how they define the world model.
- Break down the task:Separate detection, assessment, and action to achieve precise results.
- Use concrete examples:Include code snippets or logs to guide the model.
- Iterate:Refine prompts based on outputs, especially with rapidly evolving models.
- Stay updated:Follow the latest developments, such as AMD-Anthropic advances and new multimodal models.