Prompt Engineering
Designing the system prompts, context and evaluations that make AI outputs reliable enough to ship. Stop guessing why the AI got it wrong — design prompts and context that produce the same reliable result every time, and prove it with evals. Built for fresh IT graduates and working professionals alike; no engineering background required.
The Prompt Engineering program is a live, workshop-style course in prompt and context engineering: designing the whole information environment a model sees — system prompt, injected context, output contract — and then proving quality with evaluations. This is not a course of clever phrasing tricks. Every session ends with something you ship: a versioned system prompt with an eval attached, an extraction prompt that survives messy real documents, a judge harness that scores prompt variants automatically.
It is designed for two audiences at once. If you are a fresh IT graduate, it gives you an AI-ready, portfolio-backed skill before your first role. If you are a working professional — engineer, analyst, marketer, PM, or domain expert — it turns the AI tools you already use daily into dependable, repeatable workflows, and it is built to combine with the domain expertise you already have.
The program runs 8 weeks (~50 hours), live online and workshop-style, and closes with a portfolio reviewed 1:1 — including a reusable prompt library for your own field or job function.
Is This the Right Course for Me?
- Prompt Engineering (this course) — the entry point with the widest audience: reliable AI outputs, no heavy engineering background required.
- Generative AI & LLM Engineering — the broad builder foundation: models, RAG, fine-tuning and LLMOps.
- AI Agent Development — the specialist deep-dive: autonomous, tool-using, multi-agent systems.
What This Course Is Not
Techniques we deliberately skip, because they no longer earn their token cost on modern models:
- Role-play preambles, incantations and fake incentives
- Megaprompts that dump everything "just in case"
- Hard-coded few-shot examples for every call
- Jailbreak-style hacks
Course at a Glance
- Duration: 8 weeks | ~50 hours | live + hands-on
- Format: Live online · workshop-style · portfolio reviewed 1:1
- Prerequisite: None beyond comfort with everyday software. Python is helpful for the optional API track but not required.
- Curriculum: 8 modules, from how models read prompts to safety, security and applied domains
- Portfolio: 6 shipped artifacts plus a 1:1-reviewed capstone
Course Highlights
- Every module leads with what you build and ship, not what you "learn about"
- Prompt and context engineering: design everything the model sees, not just the wording
- Evaluation-first: golden test sets and LLM-as-judge before any optimization
- Structured outputs downstream systems can depend on — schemas, validation, repair loops
- Prompts treated as production software: versioned, reviewed, regression-tested
- Prompt injection defense and a red-team exercise against your own system prompt
- Applied patterns for coding, analysis, writing, support, research and agent instructions
Hands-on Projects & Portfolio
A production system prompt with a changelog and an eval attached to every revision.
Reliably turns messy documents into validated JSON.
Scores prompt variants automatically with a calibrated LLM-as-judge.
A before/after on a real workflow, with measured improvement.
Attack your own system prompt, then ship the fixes.
A reusable, domain-specific library for your own field or job function.
Tools & Stack
ChatGPT Anthropic Claude Google Gemini Claude Code OpenAI Playground Anthropic Console LangSmith Langfuse Promptfoo DSPy Pydantic JSON Schema Git Python (optional track)
Career Outcomes
An honest framing: prompt engineering today is mostly a force multiplier inside an existing role rather than a standalone job title. The strongest career position is prompting plus a durable specialty — and this course is built to combine with the domain you already work in. Where the skill pays:
- AI Engineer
- Applied AI Engineer
- AI Product Manager
- AI Automation / Workflow Specialist
- AI Solutions Consultant
- Content & Knowledge Systems Specialist
- Domain expert applying AI in healthcare, legal, finance, support or education
Who This Course Is For
- Final-year students and fresh IT graduates who want an AI-ready skill before their first role
- Software engineers and QA professionals getting more out of AI coding tools
- Data analysts, marketers, HR, legal, finance and support professionals automating real workflows
- Product managers and team leads writing AI feature specs and evaluating AI vendors
- Anyone who has been let down by inconsistent AI output and wants a repeatable method
Material Includes
- Class recordings
- Workshop worksheets & prompt templates
- Eval datasets & grading rubrics
- Certificate
Prerequisites
- Comfort with everyday software — nothing more
- Python helpful for the optional API track, not required
Estimated Time: 6 Hours
You ship:- A failure autopsy on a real broken prompt — traced to tokens, position, sampling or context rot — plus the fixed version, tested across GPT, Claude and Gemini
- Tokens, context windows, attention and why position matters; temperature and sampling
- Context rot: why more input often means worse output
- Model-specific behaviour across GPT, Claude and Gemini; how reasoning models change what you should and shouldn't spell out
Estimated Time: 6 Hours
You ship:- A production-grade system prompt for a real task — role, constraints, success criteria, escape hatches and edge-case handling, structured with XML tags and numbered sections
- System prompt vs user message vs injected context; role and constraint design
- Task decomposition; explicit success criteria; escape hatches for when the model doesn't know
- Refusal and edge-case handling; XML tags and numbered sections for structure
Estimated Time: 6 Hours
You ship:- A technique decision card for your own workflow — which of zero-shot, few-shot, chain-of-thought, self-consistency or decomposition actually wins on your task, with cache-friendly example placement
- Zero-shot vs few-shot and when each wins; examples chosen for format rather than knowledge
- Chain-of-thought and its limits on reasoning models; self-consistency; reflection and self-critique; decomposition chains
- Prompt caching and where examples must sit for caching to work
Estimated Time: 6 Hours
You ship:- An extraction prompt that reliably turns messy real-world documents into validated JSON, with a repair loop for the failures
- JSON schemas, enums and constrained generation
- Validation and repair loops; extraction prompts that survive messy real-world input
- Designing output contracts a downstream system can depend on
Estimated Time: 7 Hours
You ship:- A context plan for a multi-step workflow — what lands in the window at each step, what deliberately stays out, with citation-forcing and a token budget
- Deciding what lands in the context window at each step — and what you deliberately keep out
- Retrieval-injected context and just-in-time examples; memory and conversation compaction
- Document and table handling; citation-forcing; token budgeting as a design constraint
Estimated Time: 7 Hours
You ship:- A golden eval set and LLM-as-judge harness that scores prompt variants automatically — plus a before/after optimization case study with measured improvement
- Building a golden test set before you optimize; pass/fail criteria
- LLM-as-judge and calibrating the judge; A/B comparison; regression testing so a "small tweak" can't silently break yesterday's behaviour
- Automated prompt optimization with DSPy-style tooling and where it beats a human
Estimated Time: 6 Hours
You ship:- A versioned prompt repository — templates, variables, changelog, an eval result attached to every change, and a prompt-review checklist your team can adopt
- Version control and changelogs for prompts; an eval result attached to every change
- Prompt templates and variables; environment and model migration
- Cost and latency trade-offs; model routing; reviewing a prompt the way you'd review code
Estimated Time: 6 Hours
You ship:- A prompt-injection red-team exercise against your own system prompt plus the fixes — and your portfolio capstone: a reusable prompt library for your own field, defended in a 1:1 review
- Prompt injection and indirect injection; jailbreak resistance; PII handling; bias and tone control; guardrails and output validation
- Applied prompt patterns for coding, data analysis, business writing, customer support and research
- Instructions for agents and AI coding tools
- What's App: +91 73-01-11-21-31 for immediate assistance during business hours.
- Email: Send your inquiries to hello@progmatrix.com, and one of our representatives will get back to you as soon as possible.
- Website: You can also use the contact form on our website to send us a message directly.