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What specific prompts should I use to train DeepSeek R1 on resume data


Training DeepSeek R1 on resume data involves crafting specific prompts that help the model understand and generate high-quality resumes. Here are some detailed prompts you can use:

1. Resume Analysis Prompt**

To start, you can use a prompt that asks DeepSeek R1 to analyze a resume and identify key elements:

"Analyze the following resume and identify the most relevant skills, qualifications, and experiences that align with the job description provided below: [Insert Resume Text Here]. Job Description: [Insert Job Description Here]."

This prompt helps DeepSeek R1 understand how to extract important information from resumes.

2. Resume Generation Prompt**

For generating a resume, you can use a prompt like this:

"Act as a professional resume writer with over two decades of expertise. Please write a resume customized to the [Job Title] position at [Company]. Utilize my professional experience spanning [X] years, describe each role with 3-5 bullet points that incorporate quantifiable achievements, and integrate the top 10 pertinent keywords from the job description. My previous positions and employers include [X, Y, and Z]. An objective statement is not necessary. [Insert the job description here.]"

This prompt instructs DeepSeek R1 to create a tailored resume based on specific job requirements and personal experience.

3. Skill Identification Prompt**

To pinpoint relevant skills for a job, use the following prompt:

"What are the important skills for this position, and what should be the professional qualifications? Job Description: [Insert Job Description Here]."

This helps DeepSeek R1 identify the necessary skills and qualifications for a specific job.

4. Resume Refining Prompt**

For refining a resume with specific achievements, you can use:

"Incorporate the following certifications, awards, and achievements into the appropriate section of the resume: [List Achievements Here]."

This prompt ensures that DeepSeek R1 includes relevant achievements in the resume.

5. Resume Formatting Prompt**

To get ideas for structuring the resume, use:

"Provide ideas for sections to add different information to create a unique resume template based on the content provided."

This prompt helps DeepSeek R1 suggest a structured format for organizing resume content effectively.

Training Process

When training DeepSeek R1, it's crucial to follow a multi-stage process similar to its development:

1. Initial Training: Start with a base model and fine-tune it with cold-start data to establish a solid foundation.
2. Reinforcement Learning (RL): Apply pure RL to enhance reasoning skills.
3. Rejection Sampling: Use synthetic data generated by the model to improve accuracy.
4. Final Fine-Tuning: Merge synthetic data with supervised data and perform another round of RL for generalization.

This process ensures that DeepSeek R1 learns from both high-quality outputs and diverse domain-specific knowledge, making it effective for resume analysis and generation tasks.

Citations:
[1] https://deepseeksguides.com/how-to-use-deepseek-to-write-a-resume/
[2] https://www.reddit.com/r/artificial/comments/1ijas54/how_to_prompt_the_deepseekr1_model/
[3] https://www.linkedin.com/posts/glencathey_check-out-how-deepseeks-r1-transparently-activity-7290398540256727040-HQaW
[4] https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it
[5] https://www.youtube.com/watch?v=kRXfddrtrmM
[6] https://apidog.com/blog/deepseek-prompts-coding/
[7] https://www.learnprompt.org/deepseek-prompts/
[8] https://docs.together.ai/docs/prompting-deepseek-r1
[9] https://www.datacamp.com/tutorial/fine-tuning-deepseek-r1-reasoning-model