Generative AI & prompt engineering
technique
Building Robust AI Guardrails and Safety Filters
Implement prompting techniques and safety layers to prevent the model from generating toxic, harmful, or biased content, ensuring ethical and responsible deployment.
Advanced
documentation
Documentation and Prompt Templating Standards
Create reusable, parameterized prompt templates and internal guidelines for enterprise-wide prompt documentation and consistent deployment across teams.
Intermediate
analysis
Ethical Implications and Bias Mitigation in Gen AI
Analyze how inherent training data bias affects model output and learn specific prompting strategies to achieve more equitable and fair responses.
Intermediate
theory
Fundamentals of Large Language Models (LLMs)
Learn the core theoretical architecture, training process, and internal mechanisms that underpin modern LLMs like GPT and Claude.
Beginner
strategy
Advanced System Prompt Engineering
Design highly restrictive and detailed initial instructions (system prompts) to dictate the model's overall behavior and operational constraints for consistent performance.
Advanced
business
Cost Optimization through Prompt Reduction
Apply strategic methods to reduce prompt and response token length without sacrificing output quality, significantly lowering operational API costs.
Intermediate
aesthetics
Creative Prompting for Visual Aesthetics (Image Gen)
Learn advanced techniques for text-to-image models (e.g., weighting, blending, stylization tags) to achieve specific artistic visions and moods.
Intermediate
application
Implementing Retrieval Augmented Generation (RAG)
Integrate external knowledge bases (vector databases) with LLMs to ground responses in specific, up-to-date domain data.
Advanced
integration
Integrating LLMs with External APIs (Tool Use)
Write specialized prompts that enable the model to correctly utilize and retrieve data from external tools or web services to execute tasks beyond its training data.
Advanced
advanced
Introduction to Tree-of-Thought (ToT) Prompting
Explore advanced multi-path reasoning frameworks that allow the model to explore multiple solution branches before converging on the optimal answer.
Advanced
optimization
Parameter Tuning: Temperature and Top_P Control
Understand and manipulate key model parameters to precisely control the creativity, determinism, and diversity of generated outputs.
Intermediate
framework
Prompt Chaining and Sequential Workflow Design
Build complex, multi-step workflows where the output of one prompt becomes the input for the next, automating long-form processes and specialized tasks.
Advanced
debugging
Prompt Debugging for Hallucination Reduction
Systematically diagnose, test, and correct prompts that lead to inaccurate, false, or invented AI outputs (hallucinations) using iterative testing.
Intermediate
craftsmanship
Prompt Engineering for Code Generation Accuracy
Learn specialized techniques (e.g., contextual examples, function signatures) to ensure generative AI produces functional, secure, and idiomatic code.
Advanced
experimentation
Rapid Prototyping and Prompt
Utilize efficient methodologies and tooling for quickly testing, comparing, and iterating on multiple prompt variations to identify the highest performing design.
Intermediate
refinement
Refining Prompts for Specific Tone and Persona
Engineer detailed personas and roles within your prompts to ensure the AI output consistently matches the desired voice, context, or professional style.
Intermediate
storytelling
Storytelling and Narrative Arc Generation
Use advanced prompting methods to generate cohesive, multi-part stories with consistent character voices, emotional arcs, and world-building details.
Intermediate
implementation
Structuring JSON and XML Outputs via Prompting
Master advanced techniques to force generative models to return clean, reliable structured data formats essential for API integration and back-end processing.
Advanced
fundamentals
The Chain-of-Thought (CoT) Method
Structure complex queries by instructing the model to use step-by-step reasoning, dramatically improving factual accuracy and logical coherence.
Intermediate
visualization
Visualization and Graph Prompting Techniques
Master the use of structural language (like Mermaid or DOT) within prompts to instruct the AI to generate diagrams, charts, and visual flow representations.
Intermediate
technique
Mastering Zero-Shot & Few-Shot Prompting
Apply foundational techniques to generate high-quality, relevant outputs efficiently, maximizing results with minimal or zero examples.
Beginner
documentation
Evaluating Prompt Performance Metrics (PPMs)
Define and track quantifiable metrics (e.g., relevance score, latency, cost) to objectively measure the efficiency and effectiveness of prompt designs.
Advanced
analysis
Prompt Injection and Adversarial
Discover common attack vectors like prompt injection and learn how to implement robust security measures and guardrails for safe AI deployment.
Advanced
theory
The Theory of Tokenization and Context Windows
Deep dive into how language is broken down into tokens and learn how to manage the constraints and limitations of the AI model's context window.
Beginner