Prompt Guide

Learn how to write high-signal search prompts.

A strong search prompt helps DINQ understand who you want to find and what evidence matters most. You do not need to include every detail upfront, but adding the right context can improve the quality of your results.

Blueprint for a high-signal search prompt

A useful search prompt usually includes three types of context:

Information layerExampleWhy it helpsWhat to include
PersonaMultimodal model researcherAnchors the core candidate profile.Role, title, seniority, and domain focus
EvidenceCVPR or NeurIPS papers, open-source projectsTells DINQ what proof of relevance to look for.Papers, venues, repositories, shipped products, OSS impact, benchmarks
ConstraintsNorth America, remote, seniorNarrows the candidate pool to practical requirements.Location, timezone, work mode, years of experience, compensation range, education

Example prompts

Short prompt:

Find AI infrastructure engineers with open-source contributions.

More detailed prompt:

Find senior infrastructure engineers in North America who have worked on LLM inference optimization. Prioritize people with CUDA, TensorRT, vLLM, or vector database experience, and look for strong open-source contributions or shipped AI infrastructure projects.

Research-focused prompt:

Find multimodal model researchers with CVPR, ICCV, NeurIPS, or ICLR publications. Prioritize candidates with recent papers, strong citation signals, and public code or project pages.

Skip refinement with complete criteria

If your initial prompt already includes the key criteria DINQ needs, DINQ can move more quickly into search without asking as many follow-up questions.

Helpful criteria include:

  • Location or timezone.
  • Target title or role.
  • Expected work experience.
  • Required education level.
  • Compensation range, if relevant.
  • Must-have or must-exclude conditions.

You can still refine the search later if the first results are too broad, too narrow, too senior, or too junior.

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