Generative AI Course

Welcome to the Generative AI Engineering track on TechLearningPRO — a complete, production-grade roadmap from your very first prompt to building multi-agent enterprise AI system…

Artificial Intelligence (AI) is the field of building software that performs tasks normally requiring human intelligence — understanding language, recognising images, making dec…

Generative AI is the family of AI models that create brand-new content — text, images, audio, video, code — instead of only classifying or predicting.

These three terms are often used interchangeably, but they're actually nested: Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence.

AI didn't appear in 2022 with ChatGPT — it has a 70-year history of breakthroughs and 'AI winters' (funding droughts when hype outran reality).

A neural network is a stack of math functions loosely inspired by neurons in the brain.

A Large Language Model (LLM) is a neural network trained on enormous amounts of text to predict the next token (chunk of a word).

LLMs don't see words — they see tokens (small chunks of text) converted into embeddings (lists of numbers that capture meaning).

Training an LLM happens in three stages: pre-training on the entire internet (predict the next token), supervised fine-tuning on curated examples, and reinforcement learning fro…

Generative AI is already embedded in products you use daily.

Prompt engineering is the craft of writing inputs that consistently get good outputs from an LLM.

Production prompts almost always follow the same skeleton: system message → context → instructions → examples → input → output format.

Zero-shot prompting means asking the model to perform a task with no examples — just a clear instruction.

Few-shot prompting means including 2–5 input/output examples in the prompt before asking the model to do the task.

Chain-of-Thought (CoT) prompting asks the model to reason step-by-step before giving its answer.

The system prompt is a special message at the top of the conversation that defines the assistant's persona, capabilities and rules.

Persona prompting assigns the AI a specific role — 'You are a senior backend engineer' or 'You are a friendly nutrition coach'.

Prompt optimisation is the systematic process of improving prompts using evaluation, A/B testing and iteration.

Production AI features almost always need structured output — JSON, markdown tables, XML, code blocks — that downstream code can parse.

After thousands of production prompts, a few patterns emerge as universal best practices.

An LLM (Large Language Model) is a transformer-based neural network trained on internet-scale text to predict the next token.

The Transformer (Vaswani et al., 2017) is the neural-network architecture behind every modern LLM.

Attention lets the model decide, for each token, which other tokens matter most.

An LLM generates text one token at a time.

Embeddings are vectors (lists of numbers) that capture the meaning of text.

The context window is the maximum number of tokens an LLM can read in a single call (prompt + response combined).

Fine-tuning updates an existing LLM's weights on your domain-specific data so it learns your tone, format and knowledge.

Open-source LLMs (Llama, Mistral, Qwen, Gemma, DeepSeek) let you self-host, fine-tune freely and avoid per-token API costs.

Choosing the right LLM is a cost/quality/latency trade-off.

LLMs are powerful but flawed.

Building an AI chatbot is the 'hello world' of generative AI.

The OpenAI API is the most-used LLM API in the world.

Anthropic's Claude API is the second-most-used LLM API.

Hugging Face is the GitHub of AI — 1M+ open models, datasets and demo apps.

Building great AI UIs requires more than a chat box.

An AI workflow is a multi-step pipeline that combines LLM calls, retrieval, tool calls, and conditional logic to solve a task.

AI automation = letting an LLM perform end-to-end work that previously needed humans: triaging tickets, drafting emails, generating reports, processing invoices.

Generating high-quality content at scale — blog posts, ad copy, product descriptions, emails — is one of the most lucrative applications of LLMs.

An AI SaaS is a subscription product where the core value is an AI workflow.

AI coding assistants — Copilot, Cursor, Claude Code, Aider, Continue — have changed how developers work.

Retrieval-Augmented Generation (RAG) is the most important pattern in production AI.

Embeddings power RAG.

A vector database stores embeddings and finds nearest neighbours fast — the core operation behind RAG, semantic search, and recommendation.

Semantic search finds documents by meaning, not keywords.

Pinecone is the most popular managed vector database.

Chroma is an open-source, embedded vector database that runs in-process or as a server.

Retrieval is the 'R' in RAG and the most under-invested part of most pipelines.

Chunking splits documents into smaller pieces before embedding.

AI memory lets a chatbot remember past conversations across sessions.

Shipping RAG to production means thinking about indexing pipelines, freshness, evals, observability, multi-tenancy, security and cost.

An AI agent is an LLM that can use tools and take actions in a loop until a goal is achieved.

An agent workflow combines multiple agents, tools, and memory to solve complex tasks.

Tool calling (a.k.a.

An autonomous AI system works with minimal human supervision — it plans, executes, and self-corrects.

Multi-agent systems use multiple specialised agents that collaborate — researcher, planner, coder, critic.

AI planning = breaking a high-level goal into ordered steps before execution.

An AI automation pipeline triggers on events (new email, new lead, new ticket), runs an agent or workflow, and writes results back to your systems.

Research agents answer hard questions by searching the web (or your docs), reading multiple sources, synthesising, and citing.

AI productivity systems automate the small, draining tasks office workers do all day: emails, meeting notes, todos, calendar, summaries.

Enterprise AI agents combine domain knowledge, integrations (Salesforce, SAP, ServiceNow), governance, and SSO.

Deploying an AI feature ≠ deploying a regular API.

Where you host LLM inference matters: managed APIs (OpenAI, Anthropic), unified gateways (Vercel AI Gateway, OpenRouter), GPU hosts (Together, Groq, Fireworks), or your own GPUs.

Most AI features are exposed as APIs.

AI apps need extra observability: prompt logs, token usage, latency per provider, hallucination rate, user feedback.

AI optimisation = making your features faster, cheaper, smarter without quality regression.

AI costs scale with users — fast.

AI introduces new attack surfaces: prompt injection, data exfiltration, jailbreaks, model abuse.

Scaling AI features means handling traffic spikes, provider rate limits, multi-region deployments and graceful degradation when the LLM is slow.

A production AI workflow is more than the LLM call — it includes ingestion, evals, observability, deploys, rollbacks, and feedback loops.

Enterprise AI architecture combines secure data layers, governance, identity, audit, multi-tenancy, observability and integrations — wrapped around the AI workflow.

Practical exercises are the fastest way to internalise everything you've learned.

Targeted challenges to sharpen your prompting skills.

Build multi-step AI workflows for real-world problems — these challenges force you to use routers, parallel calls, reflection, and human-in-the-loop.

Architecture-design exercises for senior AI engineering interviews.

Debugging AI is harder than debugging code — outputs are stochastic.

A consolidated bank of common AI engineer interview questions across fundamentals, prompting, RAG, agents, deployment and architecture.

Bigger end-to-end mock projects to prove production AI skills on your portfolio.

Real production case studies — read these to understand how leading AI products were actually built and what trade-offs they made.

Scenario-based questions test your judgment under realistic constraints.

Bigger, multi-week enterprise scenarios that mirror real consulting / staff-engineer work.