Course Objective & Scope
Effective collaboration on AI initiatives requires understanding what happens beneath the chat interface. This course explains the core mechanics of generative AI—including transformer architectures, attention, pre-training, post-training alignment, and reasoning models—in accessible, non-coding terms.
Participants explore how multimodal models process text, images, audio, and code, how parameters like temperature and context window shape behavior, and when to use prompting, RAG, or fine-tuning. Hands-on exercises help teams match real organizational problems to the right generative AI pattern.
Who Should Attend?
- Business analysts, product owners, and domain specialists seeking a solid conceptual grasp of generative AI mechanics
- Project managers and coordinators who bridge business stakeholders and engineering teams
- Professionals involved in evaluating AI vendors, tools, or internal adoption programs
- No programming background required
Key Takeaways
- Explain the lifecycle of foundation models from pre-training and instruction tuning to reasoning models
- Analyze how tokenization, embeddings, semantic search, and context windows impact model performance
- Evaluate multimodal capabilities across text, vision, audio, and document understanding
- Select the appropriate adaptation pattern—prompting, RAG, or fine-tuning—for specific corporate scenarios
- Configure system instructions and generation parameters such as temperature for predictable outputs
Curriculum
Foundation Models and the Transformer Architecture
- The transformer breakthrough, self-attention, and how models capture contextual meaning
- From base pre-training to instruction tuning, RLHF, and test-time reasoning models
- Comparing frontier commercial APIs with open-weight model families (Llama, Qwen, Mistral, Gemma)
Tokens, Embeddings, and Generation Parameters
- How tokenization works and its impact on multilingual context usage, latency, and cost
- Embeddings explained: converting text into numerical vectors for semantic similarity search
- Controlling model behavior with system prompts, temperature, top-p, and context window limits
The Multimodal Generative AI Landscape
- Vision-language models for chart/document understanding and image generation architectures
- Speech-to-text, expressive text-to-speech, and real-time voice interaction models
- Code generation and sandboxed execution environments for automated calculation and data tasks
Enterprise Adaptation Patterns: Prompting, RAG, and Fine-Tuning
- Why base models lack corporate context and real-time factual grounding
- Decision framework: in-context prompting vs. Retrieval-Augmented Generation (RAG) vs. fine-tuning
- Conceptual introduction to function/tool calling and autonomous AI agents
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