Prompt Engineer
4 days ago
About the Project
Implementing solutions in advanced technology streams and its sub systems with Gen AI technologies
Responsibilities
Rapid Prompt Developing and Refinement - Develop and refine prompts to achieve specific coding goals, such as generating code snippets, fixing bugs, or writing unit tests.
Context and Constraint Management - Understand how to provide Copilot with the necessary context (e.g., code base, language, framework) and constraints (e.g., specific libraries, coding style) to guide its suggestions.
Prompt Experimentation and Testing - Conduct experiments to evaluate different prompt strategies and identify best practices.
Prompt Documentation and Library Creation - Create and maintain a library of effective prompts or prompt chains for various tasks.
User Education and Training - Develop tutorials and resources to help developers learn and improve their prompt engineering skills.
Feedback and Iteration - Analyze Copilot's responses and identify areas for improvement, suggesting updates to the model or prompting techniques.
Team Collaborate – Collaborate with product teams, data scientists, and engineers to understand user needs and translate them into effective prompts.
Prompt Experiments – Conduct experiments and research to test new prompting techniques and optimize existing workflows.
Keeping ahead on Strategy – Stay up to date on the latest advancements in national language processing (NLP) and AI, and apply those insights to your work.
Documenting to enable sharing of knowledge and Best Practices - Document and communicate your work clearly and concisely to technical and non-technical audiences for future reference
Skills/Requirement
Hands-on experience with AI models and prompt engineering is invaluable.
Proficiency in Python is highly recommended, as it's widely used in AI development and NLP tasks.
Exposure to Platforms like Coursera, edX, and others offer courses on AI, NLP, and prompt engineering.
Familiarity with tools like TensorFlow or PyTorch can be beneficial.
Analyzing AI outputs to identify what works and what needs improvement is crucial for prompt refinement.
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