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HIGAET Academy
Academy · AI & Generative Intelligence · intermediate

HIGAET Generative AI Engineering

Learn prompt design, LLM APIs, embeddings, and vector search while building chatbots, summarizers, and multimodal prototypes through guided practical training.

Duration

12 weeks · 5-7 hours/week

Level

Intermediate

Delivery

Hybrid

Status

Open for enrollment

Introduction

Why this technology matters.

Generative AI engineering is the discipline of turning large language models into software people can rely on. The model is only one component: around it sit prompts, retrieved knowledge, tools, memory, guardrails, evaluations, and deployment pipelines. This course teaches that full stack, not just the model call.

These systems matter because they change what software can do. Traditional programs follow rules written in advance; generative systems interpret open-ended requests, work with unstructured documents, draft content, call tools, and carry multi-step tasks forward. Organizations use them for support assistants, knowledge search, drafting workflows, data extraction, and developer tooling.

The field moved fast: from research transformers to chat models, then to retrieval-grounded assistants, tool-using agents, and evaluation-driven operations. Each wave made the engineering around the model more important, not less. Knowing which model to call is table stakes; knowing how to ground, test, secure, and operate it is the profession.

Generative AI solves language-shaped problems: summarizing, drafting, classifying, extracting, translating, and conversing over your own data. It does not solve problems it cannot verify: it will confidently invent citations, dates, and facts unless retrieval, constraints, and evaluation hold it accountable. By the end of this course you will have designed and deployed a grounded assistant with tools, tests, and monitoring — and you will know exactly where its limits are.

A retrieval system at a glance: User Question → Retriever → Knowledge Base → Relevant Documents → LLM → Generated Answer. Each arrow is an engineering decision: what to chunk, what to embed, what to retrieve, what to cite, and what to measure.

Why this course exists

Calling an AI API takes minutes; shipping an AI product takes engineering. Between the demo and production sit retrieval quality, prompt robustness, tool reliability, evaluation, safety, cost control, and operations — and each one fails in ways the others cannot catch. This course exists to teach the full chain: Model to Prompt to Context to Retrieval to Tools to Agents to Evaluation to Security to Infrastructure to Production, with a working system at the end that proves every link.

Overview

Know exactly what you're signing up for.

Who is this for

StudentsCareer changersSoftware developersBackend developersAI engineersEntrepreneurs

Prerequisites

  • No previous generative AI experience required
  • Basic Python familiarity for guided labs
  • Comfort using web APIs and JSON
  • Laptop with internet access for cloud-based exercises

Technologies & tools

LLM APIsEmbedding modelsVector databasesPrompt templatesOutput schemasContainersChat APIs

Skills you'll gain

Prompt designLLM API integrationEmbedding workflowsVector searchOutput structuringService deploymentResponse logging
Curriculum

A 12 weeks arc, module by module.

  1. Module 01

    Module 01 — Foundations: How transformers, tokens, and LLM APIs work

  2. Module 02

    Module 02 — Prompt Engineering: Patterns, templates, and structured outputs

  3. Module 03

    Module 03 — Core: Embeddings, chunking, and vector database workflows

  4. Module 04

    Module 04 — Engineering: Function calling and tool-connected assistants

  5. Module 05

    Module 05 — Engineering: Document Q&A and summarization pipelines

  6. Module 06

    Module 06 — Advanced: Multimodal inputs, images, and audio handling

  7. Module 07

    Module 07 — Production: Deployment, monitoring, cost control, and safety filters

  8. Module 08

    Module 08 — Capstone: Design and deploy a grounded generative AI product

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.

generative aillm apisprompt engineeringembeddingsvector databaseschatbotsai application developerhigaet academy
Outcomes

What you'll be able to do.

  • Build production-style LLM features using chat, completion, and embedding APIs
  • Design structured prompts, templates, and output schemas for reliable responses
  • Develop retrieval-grounded assistants backed by curated knowledge sources
  • Deploy containerized generative AI services with logging and versioning
  • Integrate function calling, file handling, and third-party APIs
  • Evaluate response quality with task-based rubrics and regression checks
  • Secure API keys, redact sensitive data, and apply usage controls
  • Optimize token usage, latency, and cost across model selections
Projects

You will build.

Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.

  1. Project 01

    Structured prompt template library

  2. Project 02

    Document summarizer service

  3. Project 03

    Retrieval-grounded chatbot

  4. Project 04

    Multimodal prototype assistant

  5. Capstone

    Containerized generative AI chatbot service

Key concepts

Speak the language first.

Token
The smallest unit of text a language model reads and writes. A word may split into several tokens; models count usage and cost in tokens.
Embedding
A list of numbers that captures the meaning of a piece of text. Similar meanings sit close together, which lets systems search by meaning instead of keywords.
Transformer
The neural network architecture behind modern language models. Its attention mechanism weighs which parts of the input matter most for each output.
Context window
How much text a model can consider at once, measured in tokens. Longer windows hold more context but cost more and demand careful management.
Prompt
The instruction and context given to a model to shape its output. Good prompts are specific, structured, and testable.
RAG
Retrieval-Augmented Generation: answering from retrieved documents instead of memory alone, so responses stay grounded and citeable.
Vector database
A database optimized for similarity search over embeddings. It powers the retrieval step in RAG and semantic search features.
Agent
A system that plans multi-step work, calls tools, observes results, and adjusts. Agents combine language models with software around them.
Tool calling
Giving a model access to functions such as search, databases, or APIs, so it can act on the world instead of only producing text.
Evaluation
Measuring whether an AI system does its job: offline test sets, online monitoring, and human review of real outputs.
Case studies

Learn from real engineering decisions.

Case study 01

University regulations assistant

Problem: Students ask the same questions about deadlines, eligibility, and procedures every term, and staff answer from memory or scattered PDFs, which is slow and sometimes inconsistent.

Approach: The team exports official regulations to plain text, splits them into short sections, converts each to embeddings, and stores them in a vector index. At question time the assistant retrieves the top matching sections, passes them to the model with an instruction to answer only from the provided text and cite the source section, and logs every answer with its citations for review.

Outcome: Students get instant answers with visible sources, staff handle fewer repetitive queries, and every response is traceable to an official document, which builds trust in the system.

Case study 02

Support copilot with human approval

Problem: A support team answers repetitive product questions across channels, and response quality varies by agent, shift, and workload.

Approach: The team connects resolved ticket history to a retrieval index, drafts answers with source links for agents to approve, and adds an evaluation set of past tickets so every prompt or model change is regression-tested before release. Escalation rules route low-confidence answers to humans.

Outcome: First-response time drops, answers stay consistent across agents, and the evaluation suite catches quality regressions before customers ever see them.

Case study 03

Editorial drafting workflow

Problem: A content team produces similar briefs, summaries, and reports every week, and quality depends entirely on who is writing that day.

Approach: Editors define reusable prompt templates with fixed output schemas, connect a retrieval layer over the style guide and past articles, and review a weekly sample of outputs against a quality rubric before the templates are promoted to the whole team.

Outcome: Drafting cycles shorten, output format stays consistent across writers, and the rubric reviews give the team a shared definition of good output.

Hands-on labs

Learn by building, step by step.

  1. Lab 01

    Call an LLM API and compare outputs across temperatures and system prompts

  2. Lab 02

    Design a structured-output schema and validate responses against it

  3. Lab 03

    Build an embedding pipeline and inspect nearest-neighbor quality by hand

  4. Lab 04

    Chunk a document collection three ways and measure retrieval recall for each

  5. Lab 05

    Assemble a cited question-answering endpoint over your documents

  6. Lab 06

    Add function calling so the assistant can query a live data source

  7. Lab 07

    Wire conversation memory with a fixed context budget and summarization

  8. Lab 08

    Write an offline evaluation set and score two prompt versions against it

  9. Lab 09

    Add input-output guardrails, redaction, and usage logging

  10. Lab 10

    Containerize the service and ship it with health checks and cost tracking

Keep going

Fix, check, and go deeper.

Troubleshooting & common mistakes

RAG returns irrelevant documents.

Split shorter with overlap, add metadata filters, rewrite the query before retrieval, and compare recall across chunk sizes on a fixed question set.

The model ignores the required output format.

Constrain the output with a schema, validate every response, retry with repair instructions, and fall back to a safe default when validation fails twice.

Costs spike as conversations get longer.

Shorten history with summarization, set a token budget per component, cache repeated context, and route simple turns to a smaller model.

A prompt tweak silently degrades quality.

Pin versions, keep a golden evaluation set, run it on every prompt or model change, and review diffs before promoting to production.

Answers sound confident but contain invented facts.

Require citations from retrieved text, refuse when nothing relevant is found, and log unanswered questions so gaps in the knowledge base become visible.

Tool calls fail midway through agent runs.

Add timeouts and retries with backoff, make tool calls idempotent, cap the agent's step count, and require approval for irreversible actions.

Before you move on, you should be able to

  • Explain tokens, embeddings, and context windows in plain language
  • Design a structured prompt with a validated output schema
  • Build a retrieval pipeline with chunking and a vector index
  • Connect a model to a real tool with error handling
  • Write an offline evaluation and interpret its failures
  • Name three failure modes of ungrounded generation and their fixes
  • Describe the cost and latency levers of a production AI feature

Continue learning

  • Attention Is All You Need — Vaswani et al. (foundational paper)
  • HIGAET LLM Engineering — go deeper on models and fine-tuning
  • HIGAET RAG Application Engineering — production retrieval systems
  • HIGAET AI Evals Engineering — measurement and guardrails
  • HIGAET Agentic AI Engineering — agents and orchestration
  • Your vector database documentation — index types and filtering
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