How to Master DeepSeek R1 Prompt Engineering

Understanding Deep Seek and Prompt Engineering

Introduction to Deep Seek

• The speaker aims to address the lack of discussion on effective prompt engineering for Deep Seek models, promising a comprehensive guide from advanced to basic models.

• A crash course will be provided on what to do and avoid when using different versions of R1 models, which have unique performance characteristics compared to traditional reasoning models.

Overview of Deep Seek Models

• Deep Seek is an open-source model developed by a Chinese firm, designed to compete with expensive closed-source models like OpenAI's GPT-3. It reportedly requires significantly less training cost.

• The architecture allows for reasoning during both prompting and completion phases, differing from typical models that only reason after receiving a prompt.

Model Variants and Performance

• Various quantized or distilled versions of the model exist; compressing the model reduces performance but increases accessibility for local installation.

• The speaker recommends the 14 billion parameter version as it balances size (around 9 GB) and performance effectively for average computers.

Installation Recommendations

• For those looking to install locally, the 14 billion parameter model is suggested due to its manageable size and functional performance observed during practical use.

• If the 14 billion model proves too heavy, alternatives include the 7 billion or 8 billion parameter versions; however, lower-tier options may not be as effective for complex tasks.

Language Proficiency and Prompting Techniques

• The R1 models are proficient in English and Chinese but require intentional prompts for output in other languages; results in languages like French or Spanish may not be optimal.

• Zero-shot prompting is emphasized: concise prompts without examples yield better results than lengthy contextual setups traditionally used in earlier models.

Structuring Prompts Effectively

• Focus on high-value tokens per sentence when describing problems or opportunities; this approach contrasts with previous methods that relied heavily on context.

• Markdown or XML structures are preferred by these models; however, there are nuances that need attention when formatting prompts correctly.

Temperature Settings and Context Requirements

• Temperature settings influence creativity: a range between 0.5 to 0.7 yields optimal responses balancing creativity with relevance.

Understanding Structured Prompts in AI

The Importance of Structured Formats

• The use of structured formats like markdown or XML is beneficial for AI training, as it recognizes distinct activities through tags such as "think" and "answer."

• When prompting the AI, it's suggested to first instruct it to think by identifying the country before providing an answer, enhancing clarity in responses.

Model-Specific Tips for Effective Prompting

• The largest model demonstrates complex reasoning capabilities similar to OpenAI's models, excelling in coding and math tasks.

• It can generate detailed outputs, such as product requirement documents, showcasing its ability to handle intricate prompts effectively.

Research Capabilities and Nuanced Thinking

• This model is adept at research tasks and has recently been enabled for perplexity testing, allowing experimentation beyond local computing environments.

• Its thought patterns may differ from those of other models; users might receive unexpected yet refreshing outputs that reflect a unique reasoning style.

Performance Across Different Model Sizes

70B Model Insights

• The 70B model maintains coherence but requires more intentional prompting compared to larger models.

• Users are encouraged to provide specific instructions on how the AI should think about problems, such as brainstorming customer retention strategies.

32B Model Limitations

• While still capable, the 32B model struggles with multi-step reasoning tasks. A suitable prompt could involve generating creative taglines for branding campaigns.

14B Model Features

• The speaker uses a local setup with the 14B model (Deep Seek), which allows document uploads—a feature not available in some larger models.

• Users can observe real-time thinking processes when asking questions; this informal dialogue style differs significantly from traditional AI interactions.

Recommendations for Lower-End Models

7B and 8B Models Usage

Performance Degradation in AI Models

Understanding Model Performance

• The performance of AI models significantly degrades when going below 14 billion parameters, leading to outputs that diverge from the parent model's personality.

• A model with 1.5 billion parameters is deemed not worth downloading or using; alternatives like Llama versions (7B or 8B) are recommended for better reasoning capabilities.

Custom GPT for Prompt Creation

• A custom GPT has been developed to assist users in creating effective deep seek prompts, ensuring they can achieve their desired outcomes.

• Users can input specific tasks into the custom GPT, which will guide them through selecting the appropriate model and crafting a tailored prompt based on their needs.

Output Formats and Recommendations

• The custom GPT provides outputs in both markdown format and a structured thinking tag format, offering clear instructions and recommendations for optimal usage.

• The markdown output includes concise guidelines on writing semi-formal emails introducing new products, along with suggestions on temperature settings and maximum tokens.

Comprehensive Cheat Sheet

• A detailed cheat sheet is available that outlines best practices for using various models, including eligible prompts tailored to each model's complexity level.