How to maximize the potential of new LLMs like DeepSeek V4.1-Flash and Kyutai REKI in 2026
Why prompt engineering has changed this year
Old rules based on simple “ask and you’ll receive” requests no longer work with models that understand multimodal memory, integrated search APIs, and direct action capabilities. New LLMs reward prompts that are:
- Structured(with explicit roles, constraints, and formats)
- Context-aware(tailored to model limits and external capabilities)
- Iterative(using chains of thought, error correction, and iteration)
Below are proven techniques that already work with DeepSeek V4.1-Flash, Kyutai REKI, and Harness2.
1. Structured system instructions and dynamic roles
These models respond consistently when given a system instruction that defines the tone, audience, and desired output structure.
<fragment>You are an expert data science assistant who creates clean, modular notebooks. When responding:
- Provide only Python code (no surrounding text).
- Include comments explaining the logic of each section.
- Follow PEP-8 and use type-hints where appropriate.
- Output in markdown with a code block marked as "python".</fragment></plaintext></code><p>This instruction works particularly well with DeepSeek V4.1-Flash, thanks to its low-latency memory, while Kyutai REKI uses the same structure for robotic manipulation tasks.</p><h3 id="2-prompt-chaining-and-multimodal-chains-of-thought">2. Prompt chaining and multimodal chains of thought</h3><p>When an LLM needs to move from an abstract concept to a concrete action (e.g., generating a prompt for a VLA model), break the task into a chain of small prompts.</p><code><plaintext><fragment>Step 1: Analyze the image and write a caption.
Step 2: Transform the caption into a robot command.
Step 3: Validate the command against robot syntax.</fragment></plaintext></code><p>Each step is a separate prompt, allowing the model to correct errors before moving to the next step. Harness2 excels at connecting these steps because it can refine its own harness during execution.</p><h3 id="3-structured-json-output-and-validation-schemas">3. Structured JSON output and validation schemas</h3><p>Most new APIs (especially DeepSeek’s search API) return results in JSON. Send a prompt that explicitly requests this format and includes a validation schema.</p><code><plaintext><fragment>Return a JSON array with objects containing:
{
"title": string,
"link": url,
"snippet": string,
"score": number
}
Ensure each object complies with the provided JSON-Schema.</fragment></plaintext></code><p>When paired with DeepSeek V4.1-Flash’s search capabilities, this prompt delivers relevant and immediately usable search results.</p><h2 id="when-to-apply-these-techniques">When to apply these techniques</h2><p><strong>When to use multimodal chains of thought:</strong>when combining text, images, and robotic actions (e.g., guiding REKI in object manipulation).<br><strong>When to use JSON output:</strong>when needing structured data for subsequent pipelines (ETL, dashboards, analytics).<br><strong>When to use dynamic system instructions:</strong>when changing audience or context based on conversations.</p><h2 id="practical-example-generating-a-prompt-for-kyutai-reki">Practical example: Generating a prompt for Kyutai REKI</h2><p>Goal: Have REKI grasp a colored block from a desk.</p><p>Complete prompt (broken down for clarity):</p><code><plaintext><fragment></fragment></plaintext></code><p>This prompt works seamlessly with REKI because it aligns the output format with its execution engine and provides clear constraints that the VLA model can respect.</p><h2 id="key-takeaways">Key takeaways</h2><ul><li><strong>Structure prompts with explicit roles and formats.</strong>New LLMs respond consistently when you clearly define what you’re asking.</li><li><strong>Break complex tasks into chains of thought.</strong>Chain-of-thought and multimodal chaining leverage DeepSeek’s low-latency memory and Kyutai’s action engine.</li><li><strong>Request JSON with schemas.</strong>Structured data is essential for integrating with modern APIs and Harness2.</li><li><strong>Test and iterate quickly.</strong>Use DeepSeek’s search API for feedback-driven iterations and refine system instructions based on performance.</li></ul><h2 id="conclusion">Conclusion</h2><p>Prompt engineering in 2026 is no longer about tricks for static models. It’s about designing dynamic workflows that leverage the memory, external capabilities, and action capabilities of the latest LLMs. By mastering the techniques above, you can unlock the full potential of DeepSeek V4.1-Flash, Kyutai REKI, and Harness2, transforming simple prompts into powerful AI pipelines that work reliably in the real world.</p>IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present.
CURRENT CONTEXT (October 2026):
Here are some recent trends and news for inspiration:
- DeepSeek Just Released V4.1-Flash-1017, Scores 77.0 on DeepSWE, Opens Search API: DeepSeek has just released DeepSeek-V4.1-Flash-1017, a new checkpoint of its V4.1-Flash model. It is live on the DeepSeek website, app and API. DeepSe... [2026-10-17]
- How to Make Zero-Training Tabular Predictions with NVIDIA Structured Data Models: In this tutorial, we implement Structured Data Models, NVIDIA’s new library that puts several pretrained foundation models for tables behind one PyT... [2026-10-18]
- Meet Voxel51 3.0: A Unified Multimodal Data Platform for Physical AI: Voxel51 3.0 merges search, curation, annotation and evaluation of multimodal sensor data into one platform for physical AI teams. It adds Voxel51 Agen... [2026-10-18]
Use this current information as inspiration to create an original and relevant prompt for 2026. </div>
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<a href="#how-to-maximize-the-potential-of-new-llms-like-deepseek-v4-1-flash-and-kyutai-reki-in-2026">How to maximize the potential of new LLMs like DeepSeek V4.1-Flash and Kyutai REKI in 2026</a>
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<a href="#why-prompt-engineering-has-changed-this-year">Why prompt engineering has changed this year</a>
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