An intensive 2-day program that teaches practical, operationally focused prompting methods that elevate the reliability and quality of large language model (LLM) outputs.
Why This Course Matters:
AI is rapidly shifting from novelty to necessity. Analysts and engineers must know how to safely design, validate, and operationalize LLM-driven workflows.
If your work involves translating ambiguous requirements into structured, repeatable processes, or if you rely on precision and defensibility, this course will teach you how to optimize your workflow for efficiency and reliability.
Prompt Engineering equips you with practical, real-world prompting skills that will immediately improve productivity, enhance mission outcomes, and strengthen the reliability of AI-supported decisions.
Who Should Attend:
This course is ideal for:
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Intelligence & operational analysts
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Data analysts & data engineers
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Software engineers & system integrators
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Cybersecurity practitioners
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Technical writers & knowledge managers
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Anyone supporting AI/ML-enabled mission workflows
Scope Statement:
Prompt Engineering for Analysts & Engineers is an intensive 2-day program built for professionals who rely on precision, reproducibility, and defensibility in their daily workflows. Designed for analysts, engineers, data professionals, and technical leaders, this course teaches practical, operationally focused prompting methods that elevate the reliability and quality of large language model (LLM) outputs.
Through live instructor-led workshops, participants learn how to structure prompts, decompose complex tasks, evaluate output quality, and build reusable, automation-ready workflows. The course emphasizes accuracy, safety, ethics, and verification, ensuring learners can confidently integrate LLMs into mission and production environments.
Training culminates in a hands-on capstone scenario, where learners design a complete end-to-end prompt workflow and are evaluated using an instructor-scored rubric.
During this course, you will gain the skills to:
Design structured prompting workflows
Build and evaluate reproducible LLM interactions that meet analytical and engineering requirements.
Draft high-quality prompts
Create prompts that generate consistent, context-aware, and properly formatted outputs.
Evaluate and refine AI responses
Use verification loops, evaluation criteria, and guardrails to ensure accuracy, safety, and ethical compliance.
Break down complex tasks
Translate intricate analytical or engineering problems into sequenced major and minor prompt steps.
Leverage contextual references
Integrate documents, retrieval methods, and supporting data to improve factual grounding.
Use tools and structured outputs
Employ function calling, JSON, tables, and workflow patterns to generate automation-ready results.
Develop reusable prompt templates
Apply multiple prompting strategies and patterns to build reliable frameworks for repeated use.
Operationalize your workflows
Package full prompt sequences into modular, transferable assets for team and enterprise use.
Course Format:
This course uses a modular learning approach—starting with fundamentals, moving through structured prompting and evaluation, and culminating in tool-augmented workflows and a final capstone.
A blend of live instruction, guided labs, and peer collaboration ensures each learner builds confidence in designing prompt-driven processes suitable for real-world missions and technical environments.
Capstone Assessment:
Learners will complete a scenario-based capstone demonstrating:
- Task decomposition
- Prompt sequencing
- Quality control
- Ethical and safe-use considerations
- Structured, defensible outputs
Submissions are evaluated using a standardized scoring rubric to verify competency in all core learning objectives.
Knowledge & Technology Competencies:
- Experience performing analytical or engineering tasks that require structured reasoning or workflow design
- Familiarity with basic technical concepts (data structures, systems thinking, analytic methods, or engineering processes)
- Ability to evaluate quality, accuracy, and completeness of written or technical outputs
- Access to an approved LLM environment (e.g., ChatGPT, Claude, Gemini; instructions provided)
- A laptop capable of connecting to course platforms and uploading sample files when required
