The rise of reasoning models like o1 and DeepSeek-R1 has fundamentally changed how we interact with AI. Traditional prompt engineering tactics that worked with older LLMs can actually harm performance with these advanced models. For product teams integrating reasoning LLMs into their applications, understanding these differences isn't just helpful—it's essential for product success.
This guide examines how reasoning models process instructions differently than their predecessors. You'll discover why strategies like few-shot prompting might reduce performance and when zero-shot prompting delivers superior results. We've synthesized findings from recent research papers to provide guidance based on empirical evidence rather than conventional wisdom.
The techniques covered here address real challenges product teams face when working with reasoning models. From preventing contradictory instructions to structuring multi-step reasoning processes, these approaches will help you maintain output quality while minimizing costs and development time.
In this article, we'll cover:
- 1Best practices for structuring prompts with o1 and DeepSeek-R1
- 2Common mistakes that reduce reasoning model performance
- 3Modern prompt architecture and components for 2025
- 4Strategies for embedding prompt engineering in product development
- 5When to choose prompt engineering over model fine-tuning
Best practices for structuring prompts with o1 and DeepSeek-R1
Clarity & Context
When working with reasoning models like o1 and DeepSeek-R1, instructions need to be precise yet straightforward. Unlike traditional LLMs, these models utilize built-in reasoning capabilities that work best with clear directives.
For complex tasks, state your requirements plainly. So, instead of saying "Analyze these features," try "Compare features A and B based on implementation cost and user benefit." This gives the model specific parameters to work with.
These reasoning models excel when they understand exactly what you're looking for. Actually, they often perform better with zero-shot prompting (direct instructions) rather than few-shot examples that might confuse their natural reasoning process.
Output Constraints & Format
Specifying your desired output format is especially important with reasoning models. Just because they can produce detailed reasoning steps doesn't mean they'll organize information how you want it.
For instance, if working with DeepSeek-R1, clearly outline fields you need in the response:
- Specify if you want bullet points, tables, or JSON format
- Request specific sections like "analysis," "recommendation," and "justification"
- Define any numerical constraints (e.g., "summarize in 3-5 key points")
Well, the key difference is that reasoning models will show their work by default, which can be overwhelming if not properly structured.
Balancing Minimalism vs. Step-by-Step Instructions
Research shows reasoning models respond differently to prompt length than traditional LLMs. For simple tasks, minimal prompting works best. For complex problems, explicit reasoning instructions help.
If you need a quick calculation, a brief prompt works fine. But for strategic decisions, instruct the model to "think carefully and methodically about the problem" and "take as much time as needed."
Both o1 and DeepSeek-R1 actually perform better when given permission to reason extensively on complex tasks.
Incorporating RAG (Retrieval Augmented Generation)
When combining RAG with reasoning models, less is often more. Research indicates that overwhelming these models with retrieved context can degrade performance.
If you're using RAG:
- Prioritize quality over quantity in retrieved documents
- Limit context to only the most relevant information
- Consider letting the model reason without external data first, then refine with specific facts
The balance between model reasoning and external knowledge depends on your specific use case. Sometimes the model's internal reasoning is sufficient without additional context.
Below is an example prompt written for reasoning to help you understand better.
Common Mistakes with Reasoning Models
Conflicting or Overly Detailed Instructions
One frequent mistake when working with reasoning models is providing contradictory directions. If you tell o1 to "use detailed step-by-step reasoning" but also "keep your answer under 50 words," you're creating an impossible task.
These models need consistent guidance. When the instructions conflict, they often default to their natural reasoning tendencies, which might not match your expectations.
For example, asking DeepSeek-R1 to "analyze this complex math problem thoroughly" while simultaneously requesting "just the final answer without explanation" creates confusion. The model must either ignore your constraint about brevity or skip the thorough analysis you requested.
I mean, it's like telling someone to sprint and walk slowly at the same time. Pick one approach and stick with it.
Overreliance on Few-Shot Prompting
Here's something surprising from recent research: few-shot prompting (providing examples) can actually reduce performance in reasoning models!
Multiple studies with both o1 and DeepSeek-R1 show that these models perform better with simple, direct instructions rather than multiple examples. This contradicts best practices for older LLMs, where examples improved results.
Just look at the data:
- DeepSeek-R1 explicitly notes in its documentation that "few-shot prompting consistently degrades its performance"
- The MedPrompt study found five-shot prompting led to significant decreases in o1's performance
So basically, when working with reasoning models, start with zero-shot prompting (direct instructions without examples) and only add examples if absolutely necessary.
Mixing Multiple Tasks in One Prompt
Another common mistake is cramming too many unrelated requests into a single prompt. Reasoning models excel at complex, multi-step problems, but they need focused direction.
When you ask these models to "analyze market trends AND write a product description AND calculate ROI," their chain-of-thought gets tangled. Each task requires its own reasoning path.
Instead, try breaking complex requests into sequential prompts:
- 1
First prompt
Market trend analysis - 2
Second prompt
Product description (incorporating insights from step 1) - 3
Third prompt
ROI calculation (using information from previous steps)
This approach aligns with how these models naturally process information through step-by-step reasoning.
Defining "What a Prompt Is—and Isn’t" in 2025
A Multi-Layer Instruction
In 2025, prompts for reasoning models like o1 and DeepSeek-R1 have evolved beyond simple queries. They're now structured communications with distinct components.
A modern prompt typically includes:
- 1
System instructions
Setting the model's behavior and constraints - 2
User instructions
The specific task or query - 3
Context blocks
Optional reference materials or data sources
This layered approach gives you precise control over how these reasoning models process information. It's no longer just typing "What is X?" and hoping for the best.
For example, when working with DeepSeek-R1, the system message might define reasoning requirements while the user message contains the specific problem to solve. These components work together to guide the model's reasoning process.
The difference between casual queries and professional prompts is now similar to the difference between asking a friend for advice versus submitting formal requirements to a specialized consultant.
Fine Tuning vs Prompt Engineering
Many teams struggle with whether to fine-tune their models or focus on prompt engineering. Well, the good news is that with reasoning models, prompt engineering often delivers comparable results without the complexity of retraining.
Recent research shows that properly engineered prompts can match or exceed performance of fine-tuned models in many cases. This is especially true for reasoning-heavy tasks.
For instance, one study demonstrated that GPT-4o with optimized prompts could match o1-mini's performance on code translation tasks. This suggests well-crafted prompts can unlock significant capabilities without model customization.
So, if you're weighing options, try exhausting prompt engineering approaches before committing to fine-tuning resources.
Strategic Value
Prompts have transformed from technical inputs into strategic assets. In forward-thinking organizations, they're now treated as "living documents" that encode institutional knowledge.
These prompt libraries define how advanced LLMs interpret everything from product specifications to customer communications. They ensure consistency across teams and reduce variability in AI outputs.
Just as style guides standardize writing within an organization, prompt libraries standardize AI interactions. They capture not just what to ask, but how to ask it for optimal results.
By treating prompts as strategic assets, companies maintain control over their AI systems while allowing for continuous improvement and adaptation.
Planning and Future-Proofing Your Prompts
Embedding Prompt Engineering in Product Lifecycles
Integrating prompt engineering into your product development process is now essential when working with reasoning models. This isn't just a technical task - it's a core product function.
Effective teams incorporate prompt creation and testing at multiple stages:
- During ideation: Draft initial prompts alongside feature requirements
- In development: Test prompts with diverse inputs to identify edge cases
- Post-launch: Monitor and refine prompts based on user interactions
I've seen organizations create dedicated prompt engineering roles that bridge product and engineering teams. These specialists ensure AI outputs maintain quality while satisfying business requirements.
Training your team on prompt engineering principles pays dividends. When product managers understand reasoning model capabilities, they design more realistic features. Similarly, engineers who grasp prompt nuances can build more resilient AI integrations.
Collaboration Tips
Prompt engineering works best as a collaborative exercise. No single role has all the necessary expertise to create optimal prompts for reasoning models.
A typical prompt workshop might include:
- Product managers articulating business goals and user needs
- Subject matter experts providing domain knowledge
- Engineers addressing technical constraints
- Designers ensuring outputs match user experience standards
Actually, this collaboration prevents the common mistake of creating technically sound prompts that miss business objectives (or vice versa).
Documentation is crucial here. Maintain a centralized prompt library with versioning to track changes and performance impacts. This creates an institutional memory of what works and what doesn't.
Prompt Tuning vs Prompt Engineering
Many teams rush to fine-tuning when prompt engineering could solve their problem. Research indicates that well-crafted prompts often match fine-tuned model performance, especially with reasoning models.
Consider this approach before committing to fine-tuning:
- 1Test zero-shot prompting with clear instructions
- 2If needed, modify system instructions to alter reasoning approach
- 3Only then evaluate prompt tuning (lightweight adaptation)
- 4Use full fine-tuning as a last resort
The key advantage? Prompt engineering provides immediate results without the computational cost and maintenance burden of custom model versions.
For most product applications, investing in better prompts delivers faster time-to-market and greater flexibility than pursuing model customization.
Conclusion
Reasoning models represent a significant advancement in AI capabilities, but they require a different approach to prompt engineering. The principles outlined in this article—minimalist prompting for simple tasks, explicit reasoning for complex ones, and avoiding few-shot examples—may contradict what you've learned about working with earlier LLMs.
Remember that reasoning models benefit from clear, focused instructions that align with their internal reasoning processes. By treating prompts as strategic assets and integrating prompt engineering into your product development cycle, you'll maximize the capabilities of models like o1 and DeepSeek-R1 without unnecessary customization expenses.
The most successful teams will be those who collaborate across disciplines to create prompts that balance technical capabilities with business objectives. Start with zero-shot prompting, iterate based on outputs, and only consider fine-tuning when you’ve exhausted prompt engineering approaches. This pragmatic strategy will help you deliver AI-powered features that are both technically sound and commercially valuable.