Generative AI Fundamentals
The Generative AI Fundamentals course teaches technical professionals and informed non‑specialists the core concepts of foundation models, transformers, prompt engineering, retrieval‑augmented generation, fine‑tuning, and responsible AI, emphasizing practical intuition and vendor‑neutral insight.
Who Should Take This
Product managers, data scientists, software engineers, and research analysts with at least a bachelor’s background in STEM or equivalent experience benefit from this course. They seek to understand how generative models work, design effective prompts, and apply safe, adaptable AI solutions in their projects.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
65 learning goals
1
Foundation Models & Transformers
4 topics
Evolution of Generative AI
- Describe the evolution from traditional ML to deep learning to foundation models and identify the key breakthroughs that enabled modern generative AI
- Identify the defining characteristics of foundation models including scale, self-supervised pre-training, transfer learning, and emergent capabilities
- Describe the training pipeline for large language models including data collection, pre-training, supervised fine-tuning, and alignment stages
- Analyze the computational and environmental costs of training foundation models and describe efficiency techniques including distillation, pruning, and quantization
Transformer Architecture
- Describe the transformer architecture including self-attention, multi-head attention, positional encoding, and the encoder-decoder structure at a conceptual level
- Explain how the self-attention mechanism allows transformers to capture long-range dependencies and why this was a breakthrough over recurrent architectures
- Compare encoder-only (BERT-style), decoder-only (GPT-style), and encoder-decoder (T5-style) architectures and explain which tasks each is best suited for
Types of Generative Models
- Describe different generative model families including autoregressive LLMs, diffusion models, GANs, and VAEs and identify the modalities each serves
- Describe tokenization methods including BPE, SentencePiece, and WordPiece and explain how vocabulary size affects model performance and multilingual capability
- Analyze the trade-offs between model size, inference speed, and output quality and explain how scaling laws predict performance improvements with increased parameters
Inference & Deployment
- Describe model inference optimization techniques including quantization (INT8, INT4), distillation, and speculative decoding for reducing latency and cost
- Apply model selection criteria including latency requirements, cost per token, context window size, and quality benchmarks to choose appropriate models for specific use cases
- Evaluate the trade-offs between hosting open-weight models vs using API-based commercial models considering cost, privacy, customization, and operational complexity
2
Prompt Engineering
4 topics
Prompt Design Fundamentals
- Describe the components of effective prompts including instructions, context, input data, and output format specifications
- Apply zero-shot, one-shot, and few-shot prompting techniques and explain how in-context examples guide model behavior without weight updates
- Apply system prompts and role-based framing to establish consistent model behavior, tone, and output constraints across interactions
- Evaluate the effectiveness of different prompt structures for common tasks including classification, extraction, summarization, and creative generation
Advanced Prompting Techniques
- Apply chain-of-thought prompting to improve reasoning quality and explain how step-by-step decomposition reduces errors on complex tasks
- Apply structured output techniques including JSON mode, schema enforcement, and constrained generation to produce machine-parseable model outputs
- Analyze prompt sensitivity and failure modes including prompt injection, jailbreaking, and the brittleness of complex instruction chains
- Apply self-consistency and tree-of-thought prompting to improve reliability on complex reasoning tasks through multiple solution paths
Generation Parameters
- Describe generation parameters including temperature, top-p, top-k, max tokens, and stop sequences and explain how each affects output diversity and quality
- Apply parameter tuning to achieve different output characteristics such as creative writing (high temperature) vs factual extraction (low temperature)
Prompt Testing & Evaluation
- Describe prompt evaluation methodologies including automated scoring, human evaluation rubrics, and regression testing across prompt versions
- Apply systematic prompt iteration using evaluation datasets, A/B testing, and version control to improve prompt reliability over time
3
Retrieval-Augmented Generation (RAG)
3 topics
RAG Architecture & Components
- Describe the RAG pattern including document ingestion, embedding generation, vector storage, retrieval, and augmented generation stages
- Describe text embedding models and vector databases and explain how semantic similarity search differs from keyword search
- Apply chunking strategies including fixed-size, recursive, semantic, and document-aware splitting and explain how chunk size affects retrieval quality
- Describe metadata filtering and hybrid search strategies that combine keyword matching with semantic retrieval to improve precision
RAG Pipeline Design
- Apply retrieval strategies including dense retrieval, hybrid search (keyword + semantic), and re-ranking to improve context relevance
- Analyze RAG failure modes including retrieval misses, context window overflow, and conflicting retrieved documents and describe mitigation strategies
- Evaluate RAG pipeline quality using retrieval metrics (precision@k, recall@k, MRR) and generation metrics (faithfulness, relevance, completeness)
Advanced RAG Patterns
- Describe advanced RAG patterns including multi-hop retrieval, query decomposition, self-reflective RAG, and agentic RAG with tool use
- Compare RAG vs fine-tuning for incorporating domain knowledge and analyze scenarios where each approach or their combination is most effective
4
Fine-Tuning & Adaptation
3 topics
Fine-Tuning Methods
- Describe fine-tuning approaches including full fine-tuning, parameter-efficient methods (LoRA, QLoRA, prefix tuning), and instruction tuning
- Explain how LoRA reduces fine-tuning cost by training low-rank adapter matrices instead of full model weights and describe its impact on memory and compute requirements
- Analyze the trade-offs between full fine-tuning, LoRA, and prompt engineering in terms of cost, performance, data requirements, and catastrophic forgetting risk
Alignment & RLHF
- Describe RLHF (reinforcement learning from human feedback) including preference data collection, reward model training, and policy optimization at a conceptual level
- Describe DPO (direct preference optimization) as an alternative to RLHF and explain how it simplifies alignment by eliminating the separate reward model
- Analyze the challenges of alignment including reward hacking, specification gaming, and the difficulty of capturing human values in reward functions
Training Data Preparation
- Apply data preparation techniques for fine-tuning including instruction-response pair formatting, data quality filtering, and deduplication
- Evaluate the impact of training data quality, diversity, and size on fine-tuned model performance and describe synthetic data generation as an augmentation strategy
- Apply evaluation benchmarks and human evaluation protocols to measure fine-tuned model quality against baseline and competitor models
5
Responsible AI & Safety
4 topics
Hallucination & Factuality
- Describe hallucination in generative AI including intrinsic vs extrinsic hallucination and explain why autoregressive generation produces confident but incorrect outputs
- Apply hallucination mitigation strategies including grounding with retrieved context, citation generation, confidence calibration, and human-in-the-loop verification
- Evaluate factuality assessment approaches including automated fact-checking, NLI-based verification, and LLM-as-judge evaluation frameworks
Ethical Considerations
- Identify ethical concerns in generative AI including bias amplification, deepfakes, copyright infringement, environmental impact, and labor displacement
- Analyze how training data composition affects model bias and describe approaches for measuring and mitigating demographic biases in generated content
- Apply content provenance and watermarking concepts to distinguish AI-generated content from human-created content in text, image, and audio modalities
Safety Guardrails & Governance
- Describe guardrail mechanisms including content filtering, input/output validation, rate limiting, and model-level safety training
- Apply red-teaming methodology to identify vulnerabilities in AI systems including adversarial prompts, edge cases, and safety boundary violations
- Evaluate AI governance frameworks including model cards, datasheets for datasets, and organizational AI review processes for responsible deployment
Legal & Regulatory Landscape
- Describe the evolving legal landscape around generative AI including copyright, liability, and regulatory frameworks such as the EU AI Act
- Analyze intellectual property challenges including training data copyright, model output ownership, and fair use considerations in generative AI applications
6
Applications & Use Cases
4 topics
Text Generation Applications
- Apply generative AI to text summarization, translation, and content creation tasks and describe how task framing affects output quality
- Apply generative AI to code generation, debugging, and explanation tasks and describe techniques for improving code output reliability
- Analyze the limitations of generative AI for knowledge work including factual reliability, domain specificity, and the need for human verification workflows
Multimodal Applications
- Describe multimodal generative AI capabilities including image generation, vision-language models, audio synthesis, and video generation at a conceptual level
- Analyze the strengths and limitations of current multimodal models for real-world applications such as document understanding, accessibility, and creative workflows
AI Agents & Tool Use
- Describe the AI agent paradigm including planning, tool use, memory, and reflection capabilities that extend LLMs beyond single-turn generation
- Apply function calling and tool use patterns to extend LLM capabilities with external APIs, databases, and code execution environments
- Evaluate the reliability and safety challenges of autonomous AI agents including error propagation, action irreversibility, and human oversight requirements
Enterprise Adoption
- Apply evaluation criteria for selecting generative AI solutions including cost analysis, latency requirements, data privacy, and vendor lock-in considerations
- Analyze organizational readiness for generative AI adoption including data maturity, talent requirements, change management, and ROI measurement frameworks
- Apply cost estimation techniques for generative AI workloads including token-based pricing models, caching strategies, and prompt optimization to reduce operational expenses
Hands-On Labs
3 labs
~50 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Foundation models and transformer architecture (attention mechanism, self-attention, encoder-decoder), prompt engineering techniques (zero-shot, few-shot, chain-of-thought, system prompts), retrieval-augmented generation (RAG) pipeline components and design, fine-tuning and adaptation methods (full fine-tuning, LoRA, RLHF, instruction tuning), responsible AI and safety (alignment, hallucination, toxicity, guardrails), applications and use cases across industries (code generation, content creation, search, summarization, agents)
Not Covered
- Model training code and framework-specific implementation (PyTorch, TensorFlow, JAX)
- Vendor-specific API integration (OpenAI API, Anthropic API, Google AI Studio)
- Research-level mathematics (attention mechanism derivations, RLHF reward modeling proofs)
- Model pre-training from scratch (dataset curation, compute infrastructure, distributed training)
- Hardware optimization (GPU/TPU selection, quantization implementation details)
- Academic ML research methodology and paper writing
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