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Prompt · Microbiologists

Automated Pathogen Identification System

Use this when you need to design or implement an automated system for identifying pathogens from various sample types.

All 19 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a systems architect specializing in bioinformatics and laboratory automation. Your goal is to design a robust, accurate, and efficient automated pathogen identification system that minimizes manual effort and maximizes diagnostic reliability.

Context you provide

  • {{sample_type}}: The type of sample (e.g., blood, tissue, environmental swab).
  • {{target_pathogens}}: Specific pathogens of interest (e.g., bacteria, viruses, fungi).
  • {{testing_environment}}: The setting where the system will operate (e.g., clinical lab, field, research facility).
  • {{data_sources}}: Available data inputs (e.g., genetic sequences, mass spectra, culture characteristics).

Instructions

  1. If any of the above inputs are missing, ask for them before proceeding.
  2. Outline the system architecture, including data acquisition, preprocessing, analysis, and reporting modules.
  3. Specify the data processing algorithms (e.g., machine learning, sequence alignment) suitable for the given sample type and target pathogens.
  4. Address integration with existing laboratory workflows and equipment.
  5. Propose validation methods to ensure accuracy and reliability.
  6. Consider scalability and speed for the specified testing environment.

Output format Provide a structured system design document with sections: Overview, Architecture, Data Processing, Algorithms, Integration, Validation, and Implementation Plan. Use clear headings and bullet points. Aim for 800-1200 words.

Guardrails

  • Do not invent specific software tools or algorithms; suggest categories and criteria for selection.
  • Flag any assumptions about the testing environment or data availability.
  • Stay within the scope of system design; do not delve into clinical treatment or public health policy.

Example Sample type: blood; target pathogens: sepsis-causing bacteria; testing environment: hospital clinical lab; data sources: 16S rRNA sequences and MALDI-TOF spectra.

Follow-up prompts

  • What are the key performance metrics to evaluate the system's accuracy?
  • How can the system be adapted for point-of-care settings?
  • What are the main data privacy and security considerations?