Summary of Reddit Comments on "best prompts deepseek":

DeepSeek R1 Model Overview:

• DeepSeek R1 is a highly praised model that offers great performance for various tasks.
• Some users find it ideal for tasks like storing passwords and mathematics, while others point out its mediocre performance for general use cases.

Pros and Cons of DeepSeek R1:

Pros:

• Math and academia: DeepSeek R1 excels in these areas, providing accurate and detailed responses.
• Use as a search engine: Suitable for quick knowledge retrieval, brainstorming, and ultra-learning.
• Local model advantages: Runs effectively for local AI applications when appropriately optimized.
• Password storage: Ideal for securely storing passwords.

Cons:

• Underperformance: Some users report that the model struggles with simple tasks and provides inaccurate or nonsensical responses.
• Comparison with online models: Challenges arise when comparing DeepSeek R1 with larger online models due to resource limitations.
• Limitations of local models: Lacks the context, web searching abilities, and RAG functionalities found in online models.
• Comparison to ChatGPT: Some users find DeepSeek R1 subpar in comparison to ChatGPT for certain tasks.

Recommendations for Enhancing DeepSeek R1 Performance:

• Knowledge graphs and RAG integration: Implement these to improve performance.
• Precise agentic workflows and setting up system prompts: Enhance local AI model capabilities for optimal results.
• Utilizing bigger context: Increase the context window size to improve data retrieval from documents or texts.

Comparison with Other AI Models:

• No single AI is universally superior: Each AI model (Claude, ChatGPT, Gemini, etc.) excels in various tasks.
• Consider purpose-specific models: For tasks like coding support, choosing the right model (DeepSeek R1 70B) is essential.

Concerns and Critiques Received:

• Performance criticisms: Users have pointed out the discrepancy between marketing claims and actual model performance.
• Hardware limitations: Recognized as a factor impacting the full potential of DeepSeek R1 and other models.
• Optimization challenges: Some users find it difficult to optimize local AI models for desired outcomes.
• Need for comprehensive guides: Calls for detailed resources on optimizing local AI performance to overcome challenges.

Multimodal Capabilities and Prompt Structuring:

• Users express an interest in making DeepSeek R1 multimodal to handle pictures, PDFs, and other files.
• The prompt structuring in different LLMs like DeepSeek R1 tends to yield similar outputs, especially for image tasks.

Recommendation for Handling Coding Tasks:

• Llama3.1: Identified as a superior model for coding tasks, offering satisfactory performance.

Feedback on System Prompts:

• System prompts are expected to result in similar outputs across models, reflecting similarities in response generation.

User Experience with Different LLMs:

• CoT yammering found effective in adjusting prompts for retries and enhancing the performance of the 32b q8 model.

Conclusively, while DeepSeek R1 showcases strengths in specialized areas like mathematics and academia, it faces challenges related to underperformance in general tasks and limitations in comparison to larger online models due to hardware constraints. Users emphasize the importance of precise prompt engineering, leveraging system prompts effectively, and optimizing local models for enhanced outcomes.