Explain The Basic Concepts of Generative AI

Source: Dev.to

Domain 2 – Fundamentals of Generative AI

Task Statement 2.1

Domain 1 gives you the language of AI/ML.
Domain 2 shifts the focus to something more specific and increasingly central in AWS exam content and real‑world workloads: Generative AI.

Instead of predicting a label (e.g., fraud/not fraud) or a number (e.g., next month’s demand), GenAI models generate new content—text, images, audio, video, and code—based on patterns learned from massive datasets.

This domain covers the core building blocks (tokens, embeddings, transformers, diffusion), common use cases, and the high‑level lifecycle of foundation models.

🎯 Objectives

• Define the core concepts and terminology of generative AI.
• Recognize common real‑world use cases for generative AI models.
• Describe the foundation‑model lifecycle from data and model selection through pre‑training, fine‑tuning, evaluation, deployment, and feedback.

1️⃣ Define Foundational GenAI Concepts

1.1 Tokens

• Tokens are the basic units a language model reads and writes (word, sub‑word, punctuation, whitespace).
• Why tokens matter: cost/latency and model limits are often tied to token counts (input + output).

1.2 Chunking

• Chunking splits large text into smaller segments (“chunks”) so it can be processed effectively.
• Used in Retrieval‑Augmented Generation (RAG): store/search chunks, then provide the most relevant ones to the model.
• Why chunking matters: models have context‑window limits; chunking helps fit useful information into the prompt.

1.3 Embeddings

• Embeddings are numeric representations of content (text, images, etc.) that capture meaning.
• Similar items end up with similar embeddings.
• Embeddings are used for:
  • Semantic search
  • Clustering
  • Recommendation
  • Retrieval

1.4 Vectors

• A vector is the array of numbers that represents an embedding.
• Vectors are compared with similarity metrics (e.g., cosine similarity) to find “closest meaning.”

1.5 Prompt Engineering

• Prompt engineering crafts instructions and context to guide model outputs.
• Techniques include:
  • Clear instructions
  • Role prompting
  • Few‑shot examples
  • Constraints
  • Formatting requirements
  • Grounding with retrieved context
• Why it matters: effective prompts can dramatically improve quality without retraining.

1.6 Transformer‑Based LLMs

• Modern LLMs are built on the transformer architecture.
• Core idea: attention mechanisms that let the model focus on relevant parts of the input.
• Strengths:
  • Language understanding & generation
  • Summarization
  • Extraction
  • Reasoning‑like behavior (with limitations)

1.7 Foundation Models (FMs)

• Foundation models are large, general‑purpose models trained on broad datasets and adaptable to many tasks.
• LLMs are a common type, but FMs also exist for images, audio, or multimodal tasks.

1.8 Multimodal Models

• Multimodal models accept and/or generate more than one modality (e.g., text + image).
• Example: provide an image and ask for a description, or provide text and generate an image.

1.9 Diffusion Models

• Diffusion models are widely used for image generation.
• They learn to reverse a “noising” process: start from noise and iteratively produce an image.
• Why they matter: they power high‑quality text‑to‑image generation.

2️⃣ Identify Potential Use Cases for GenAI Models

GenAI shines when you need to generate, transform, or interact with content at scale. Common real‑world use cases include:

Note: GenAI is especially strong for language/content generation or understanding unstructured data, but it is not ideal when deterministic outputs are required.

3️⃣ Describe the Foundation Model Lifecycle

Foundation models follow a lifecycle similar to traditional ML, with GenAI‑specific steps and decisions:

3.1 Data Selection

• Choose large, diverse datasets (text, images, code, etc.).
• Apply filtering, quality controls, and handle sensitive data, licensing, and safety considerations.

3.2 Model Selection

• Pick an existing FM or decide to pre‑train/customize based on:
  • Capability requirements (quality, reasoning, multimodal)
  • Latency / cost constraints
  • Domain specificity
  • Governance requirements

3.3 Pre‑training

• Train the foundation model on massive corpora to learn general representations.
• This step is expensive and typically performed by large providers.

3.4 Fine‑tuning

• Adapt the model to a specific domain or task using additional data (often smaller and higher‑quality).
• Can improve tone, format, domain knowledge, and task performance.

3.5 Evaluation

• Evaluate quality, safety, and performance:
  • Task quality (helpfulness, correctness)
  • Robustness (edge cases)
  • Bias / fairness
  • Toxicity / safety
  • Hallucination tendencies (as applicable)

3.6 Deployment

• Serve the model for