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Generative AI & Prompt Engineering: From Zero to Expert

Master modern language models — from prompting fundamentals through RAG and agents — by building eight production-grade projects.

Last updated 07/2026 English Certified Course

What you’ll learn

New to AI, or already prompting daily? This programme takes you from the fundamentals of how language models work through to building, evaluating and deploying production AI applications — with eight portfolio projects along the way.

Write reliable prompts for any large language model
Apply chain-of-thought and few-shot reasoning patterns
Build retrieval-augmented (RAG) applications end to end
Use function calling and structured output safely
Design, test and ship autonomous AI agents
Evaluate model output and reduce hallucination
Fine-tune and adapt models for a specific domain
Deploy AI features to production with monitoring

Course outcomes

What you will be able to do on completion

This programme is built around capability, not coverage. Every module ends in something you can show — by the end you will be able to:

  1. 01Explain how large language models generate text — and where they reliably fail
  2. 02Write, test and version prompts that hold up in production, not just in a demo
  3. 03Integrate model APIs with streaming, function calling and structured output
  4. 04Design retrieval-augmented systems grounded in your organisation’s own data
  5. 05Build agents that plan, use tools, and recover when a step goes wrong
  6. 06Evaluate model output systematically and measurably reduce hallucination
  7. 07Apply responsible-AI practice across bias, privacy and governance
  8. 08Deploy, monitor and cost-manage an AI feature running at real volume
  9. 09Present a portfolio of eight projects plus a mentor-reviewed capstone

Course content

8 modules · 60 sessions · 120 hours

Sessions run two hours each, live with an instructor. Each module closes with a graded build.

1.1 What artificial intelligence actually isPreview2 hrs
1.2 Machine learning refresher for practitionersPreview2 hrs
1.3 How neural networks represent language2 hrs
1.4 Tokens, embeddings and vector space2 hrs
1.5 Inside a transformer2 hrs
1.6 Pre-training, fine-tuning and alignment2 hrs
1.7 The current model landscape2 hrs
1.8 Setting up your AI workspace2 hrs
2.1 Anatomy of an effective promptPreview2 hrs
2.2 Zero-shot, one-shot and few-shot prompting2 hrs
2.3 Chain-of-thought and step-by-step reasoning2 hrs
2.4 System prompts, roles and personas2 hrs
2.5 Structured output with JSON and schemas2 hrs
2.6 Prompt patterns for summarisation and extraction2 hrs
2.7 Prompt patterns for classification and routing2 hrs
2.8 Guardrails, refusals and safe completions2 hrs
2.9 Prompt testing and version control2 hrs
2.10 Workshop: rewriting weak prompts2 hrs
3.1 Calling model APIs from Python2 hrs
3.2 Temperature, top-p and sampling parameters2 hrs
3.3 Streaming responses and partial output2 hrs
3.4 Function calling and tool definitions2 hrs
3.5 Errors, retries and rate limits2 hrs
3.6 Token accounting and cost control2 hrs
3.7 Caching and prompt reuse2 hrs
3.8 Build: a command-line AI assistant2 hrs
4.1 Why retrieval matters2 hrs
4.2 Document loading and cleaning2 hrs
4.3 Chunking strategies2 hrs
4.4 Embeddings in practice2 hrs
4.5 Vector databases and indexes2 hrs
4.6 Similarity search and re-ranking2 hrs
4.7 Building the retrieval pipeline2 hrs
4.8 Grounding answers and citing sources2 hrs
4.9 Measuring retrieval quality2 hrs
4.10 Project: a document question-answering system2 hrs
5.1 Agents versus assistants2 hrs
5.2 The reasoning-and-action loop2 hrs
5.3 Tool use and external integrations2 hrs
5.4 Short-term and long-term memory2 hrs
5.5 Planning and task decomposition2 hrs
5.6 Multi-agent collaboration2 hrs
5.7 Human-in-the-loop review2 hrs
5.8 Failure handling and recovery2 hrs
5.9 Workflow automation case studies2 hrs
5.10 Project: an autonomous research agent2 hrs
6.1 Why model output must be evaluated2 hrs
6.2 Building an evaluation set2 hrs
6.3 Automated scoring and model-as-judge2 hrs
6.4 Detecting and reducing hallucination2 hrs
6.5 Bias, fairness and data privacy2 hrs
6.6 Responsible AI policy and governance2 hrs
7.1 Packaging an AI application2 hrs
7.2 Serving models behind an API2 hrs
7.3 Monitoring, logging and observability2 hrs
7.4 Latency, scaling and cost at volume2 hrs
7.5 Security and data handling2 hrs
7.6 CI/CD for AI features2 hrs
8.1 Capstone build and mentor review2 hrs
8.2 Portfolio, resume and interview preparation2 hrs

Requirements

  • Basic Python knowledge — loops, functions and lists
  • A laptop with internet access; no GPU required
  • Curiosity about AI — no machine learning background needed
  • A free API key from any major model provider

Description

  • Learn how modern language models work, from tokens through to agents.
  • Build eight portfolio-grade projects reviewed by working practitioners.
  • Practise on real datasets and production-style codebases.
  • Finish with a verifiable certificate recognised by our hiring partners.

Instructor

NR

Dr. Neha Rao

Lead AI Instructor · TEVVO Academy
12,480 reviews 41,200 students 9 courses

Neha has spent a decade building machine learning systems in production, most recently leading an applied AI team. She teaches the way she works — starting from a real problem, then building the smallest thing that solves it.

Student feedback

4.9 Course rating
78%
14%
5%
2%
1%

Featured reviews

AS
Ananya S.

The prompt engineering module alone paid for the course. I shipped an internal AI assistant at work two weeks in.

KR
Karthik R.

Clear, practical, and no filler. The RAG project is the closest thing to real production work I have seen in a course.

MJ
Meera J.

Genuinely current material — most courses are a year behind. The agent section could go deeper, but it is excellent overall.

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