Generative AI Foundations
Build a rigorous mental model of modern Generative AI — from tokens and embeddings to transformers, fine-tuning, and evaluation.
Duration
8 weeks · 6-8 hours/week
Level
Beginner
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Generative AI is the technology behind systems that write, summarize, and answer questions in fluent language, built on tokens, embeddings, and transformer models. It matters now because these models sit inside everyday tools for support, documents, and search, so anyone working with software or content needs a rigorous mental model of how they behave.
It is used to draft text, summarize long documents, and produce structured outputs like JSON for downstream applications, and it solves the problem of fluent first drafts at speed. It does not solve missing or wrong source data: a model will invent facts when asked beyond its evidence, it does not replace domain judgment, and dashboards of demo prompts do not prove reliability.
By the end the student will be able to build small text generation and summarization prototypes with consistent prompts, structured-output generators that parse reliably, and a basic evaluation check that scores model outputs against expected examples.
Why this course exists
The gap is between a demo prompt that works once and a reliable mental model of tokens, context limits, fine-tuning trade-offs, and evaluation. The course teaches the arc from Model to Prompt to Context to Evaluation to responsible use, so students stop guessing and start designing prompts and checks that behave.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- No previous AI experience required
- Basic computer literacy and web tools
- Willingness to complete weekly hands-on exercises
Technologies & tools
Skills you'll gain
A 8 weeks arc, module by module.
- Module 01
Week 1 — The Generative AI landscape
- Module 02
Week 2 — Tokens, embeddings, and the transformer block
- Module 03
Week 3 — Prompting patterns and structured outputs
- Module 04
Week 4 — Retrieval-Augmented Generation in practice
- Module 05
Week 5 — Fine-tuning vs. adapters vs. prompting
- Module 06
Week 6 — Evaluation, eval datasets, and regression testing
- Module 07
Week 7 — Safety, guardrails, and responsible deployment
- Module 08
Week 8 — Capstone: ship a production-grade LLM prototype
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Explain how modern LLMs are trained, served, and evaluated.
- Design prompts and structured outputs for reliable LLM behavior.
- Choose between RAG, fine-tuning, and tool-use for a given problem.
- Ship a working LLM-powered prototype with sensible guardrails.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Text generation playground
- Project 02
Summarization assistant
- Project 03
Prompt library collection
- Project 04
Knowledge Q&A prototype
- Capstone
Generative AI foundations portfolio
Speak the language first.
- Tokens and tokenization
- Tokens are the small text pieces a model reads and writes; tokenization splits input text so you can predict cost and context limits.
- Embeddings
- Embeddings are number lists that capture word and sentence meaning, letting systems compare similarity between texts.
- Transformers
- Transformers are the neural network design behind modern language models, using attention to weigh which words matter most.
- Large language models
- Large language models predict likely next tokens from patterns learned in training, powering generation and summarization.
- Prompt design
- Prompt design is writing clear instructions, context, and examples so a model returns consistent, useful output.
- Structured outputs
- Structured outputs force model answers into formats like JSON or tables so applications can parse them reliably.
- Fine-tuning basics
- Fine-tuning continues training a model on task examples so it follows domain style and terminology more closely.
- Model evaluation basics
- Evaluation checks model answers against expected examples to spot errors before relying on the system.
- Responsible AI basics
- Responsible AI basics cover checking outputs for bias and errors and using models only for appropriate tasks.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Prompts return inconsistent answers across runs
Add explicit format instructions and one or two examples, then rerun the same prompt three times to confirm stability.
Summaries drop key facts from long texts
Split the source into smaller sections, summarize each, then combine, so nothing falls outside the context window.
Model invents facts for knowledge questions
Ask only what the provided text supports and add a rule to say when the answer is not in the source.
Structured output is hard to parse
Specify the exact schema with field names and types, and reject and retry any response missing required fields.
Token limits cut off long inputs
Shorten or chunk the input and count tokens first so the prompt plus expected answer fits the limit.
Before you move on, you should be able to
- Explain how tokens, embeddings, and transformers produce model output
- Design prompts that produce consistent and structured responses
- Build small text generation and summarization prototypes
- Evaluate model outputs against expected examples
- Describe when fine-tuning is appropriate for a task
- Apply responsible-use checks to everyday AI tasks
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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