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Generative AI LLMs · RAG · Structured Outputs ✔ 3 Levels Available

Prompt Engineering
Full Programme

Certification and intermediate certification tracks covering prompt design, techniques, LLMs, APIs, RAG, structured outputs, and ethics—plus an advanced syllabus spanning intermediate through frontier reasoning, multimodal prompting, domain specialisation, safety, PromptOps, and capstone projects. Format: Theory + Lab/Assignments per syllabus.

⏱
Duration
63 Hours
🧪
Lab / Assignments
36 Hours
🎯
Format
Theory + Lab/Assignments
🏆
Certificate
Arich Certified
Enrol Now →
Course Code
PE-GENAI
Click a Level to View its Syllabus
L1
PE-CERT · Certification
63 hrs · 10 Modules
›
L2
PE-INT-CERT · Intermediate Certification
10 Modules
›
L3
PE-ADV · Advanced Syllabus
22 Modules
›
📄 PE-CERT — Prompt Engineering – Certification Syllabus
L1
PE-CERT
Prompt Engineering – Certification
Total Duration: 63 Hours  |  Theory: 27 hrs  |  Lab: 36 hrs (syllabus summary)
01
Introduction to Prompt Engineering
Theory: 3 hrs  ·  Lab: 3 hrs  ·  Total: 6 hrs
▼
What is a Prompt: Definition & Types
Role of Prompt Engineering in the LLM Lifecycle
Prompt–Response Cycle: How LLMs Interpret Prompts
Parameters: Temperature, Top-p, Max Tokens
Context Windows & Token Limits
Lab: Experiment with Parameter Variations
02
Prompt Design Fundamentals
Theory: 3 hrs  ·  Lab: 3 hrs  ·  Total: 6 hrs
▼
Principles: Clarity, Specificity, Constraints
Structuring Instructions
Roles & Perspectives
Formatting (Tables, Lists, Code, Markdown)
Prompt Pitfalls & Anti-patterns
Lab: Rewrite Poor Prompts → Optimized Prompts
03
Prompting Techniques – Part 1
Theory: 3 hrs  ·  Lab: 5 hrs  ·  Total: 8 hrs
▼
Zero-Shot Prompting
Few-Shot Prompting
Chain-of-Thought Prompting
Iterative Refinement / Re-Prompting
Lab: Create Few-Shot Examples & Evaluate
04
Prompting Techniques – Part 2
Theory: 3 hrs  ·  Lab: 5 hrs  ·  Total: 8 hrs
▼
Persona Prompting
Multi-Turn Conversation & Self-Consistency
Instruction Hierarchies (System → User → Assistant)
Evaluating Prompt Outcomes
Lab: Conversational Prompt Chain
05
Understanding Generative AI & LLMs
Theory: 3 hrs  ·  Lab: 3 hrs  ·  Total: 6 hrs
▼
Evolution of Generative AI & LLMs
Embeddings, Attention & NN Basics
Transformer Architecture (E2E Overview)
Lab: Explore Token Embeddings & Similarity
06
APIs, Playground & Tools
Theory: 3 hrs  ·  Lab: 3 hrs  ·  Total: 6 hrs
▼
Using OpenAI Playground, Model Selection
API Parameters & JSON Requests
Calling LLMs via Python Scripts
Error Handling, Rate Limits, Token Counts
Lab: Build Simple Summarizer/Q&A Tool
07
RAG & Context Augmentation
Theory: 3 hrs  ·  Lab: 5 hrs  ·  Total: 8 hrs
▼
Why Context is Limited
Introduction to Embeddings
Vector Databases (FAISS, Pinecone, Chroma)
RAG (Retrieval Augmented Generation) Basics
Lab: Build Mini-RAG Demo
08
Structured Outputs + Function Calling
Theory: 2 hrs  ·  Lab: 3 hrs  ·  Total: 5 hrs
▼
Structured Output Prompting (JSON Mode)
Enforcing Output Formats & Schemas
Basic Function Calling: Concept & Workflow
Lab: JSON Output Generation + Simple Function Call
09
Capstone Project
Theory: 1 hr  ·  Lab: 5 hrs  ·  Total: 6 hrs
▼
Choose Scenario (Chatbot, Summarizer, Resume Analyzer)
Design Prompt Workflow & Build
Presentation, Evaluation & Peer Review
10
Ethics, Evaluation & Future Trends
Theory: 3 hrs  ·  Lab: 1 hr  ·  Total: 4 hrs
▼
Prompt Evaluation Metrics (BLEU, ROUGE, Qualitative)
Responsible Prompting: Bias, Safety & Misinformation
Future of Prompt Engineering Roles
Lab: Evaluate Prompt Variants
📄 PE-INT-CERT — Prompt Engineering Intermediate Certification
L2
PE-INT-CERT
Prompt Engineering Intermediate Certification
Total Duration: 63.0 Hours  |  Theory: 27.0 hrs  |  Lab/Assignments: 36.0 hrs (syllabus summary)
01
Introduction to Prompt Engineering
Theory: 3 hrs  ·  Lab/Assignments: 3 hrs  ·  Total: 6 hrs
▼
What is a Prompt: Definition & Types
Role of Prompt Engineering in the LLM Lifecycle
Prompt–Response Cycle: How LLMs Interpret Prompts
Parameters: Temperature, Top-p, Max Tokens
Context Windows & Token Limits
Lab: Experiment with Parameter Variations
02
Prompt Design Fundamentals
Theory: 3 hrs  ·  Lab/Assignments: 3 hrs  ·  Total: 6 hrs
▼
Principles: Clarity, Specificity, Constraints
Structuring Instructions
Roles & Perspectives
Formatting (Tables, Lists, Code, Markdown)
Prompt Pitfalls & Anti-patterns
Lab: Rewrite Poor Prompts → Optimized Prompts
03
Prompting Techniques – Part 1
Theory: 3 hrs  ·  Lab/Assignments: 5 hrs  ·  Total: 8 hrs
▼
Zero-Shot Prompting
Few-Shot Prompting
Chain-of-Thought Prompting
Iterative Refinement / Re-Prompting
Lab: Create Few-Shot Examples & Evaluate
04
Prompting Techniques – Part 2
Theory: 3 hrs  ·  Lab/Assignments: 5 hrs  ·  Total: 8 hrs
▼
Persona Prompting
Multi-Turn Conversation & Self-Consistency
Instruction Hierarchies (System → User → Assistant)
Evaluating Prompt Outcomes
Lab: Conversational Prompt Chain
05
Understanding Generative AI & LLMs
Theory: 3 hrs  ·  Lab/Assignments: 3 hrs  ·  Total: 6 hrs
▼
Evolution of Generative AI & LLMs
Embeddings, Attention & NN Basics
Transformer Architecture (E2E Overview)
Lab: Explore Token Embeddings & Similarity
06
APIs, Playground & Tools
Theory: 3 hrs  ·  Lab/Assignments: 3 hrs  ·  Total: 6 hrs
▼
Using OpenAI Playground, Model Selection
API Parameters & JSON Requests
Calling LLMs via Python Scripts
Error Handling, Rate Limits, Token Counts
Lab: Build Simple Summarizer/Q&A Tool
07
RAG & Context Augmentation
Theory: 3 hrs  ·  Lab/Assignments: 5 hrs  ·  Total: 8 hrs
▼
Why Context is Limited
Introduction to Embeddings
Vector Databases (FAISS, Pinecone, Chroma)
RAG (Retrieval Augmented Generation) Basics
Lab: Build Mini-RAG Demo
08
Structured Outputs + Function Calling
Theory: 2 hrs  ·  Lab/Assignments: 3 hrs  ·  Total: 5 hrs
▼
Structured Output Prompting (JSON Mode)
Enforcing Output Formats & Schemas
Basic Function Calling: Concept & Workflow
Lab: JSON Output Generation + Simple Function Call
09
Capstone Project
Theory: 1 hr  ·  Lab/Assignments: 5 hrs  ·  Total: 6 hrs
▼
Choose Scenario (Chatbot, Summarizer, Resume Analyzer)
Design Prompt Workflow & Build
Presentation, Evaluation & Peer Review
10
Ethics, Evaluation & Future Trends
Theory: 3 hrs  ·  Lab/Assignments: 1 hr  ·  Total: 4 hrs
▼
Prompt Evaluation Metrics (BLEU, ROUGE, Qualitative)
Responsible Prompting: Bias, Safety & Misinformation
Future of Prompt Engineering Roles
Lab: Evaluate Prompt Variants
📄 PE-ADV — Prompt Engineering – Advanced Syllabus (Consolidated)
L3
PE-ADV
Prompt Engineering – Advanced Programme
Total Duration: 388 Hours  |  Theory: 178 hrs  |  Lab/Assignments: 210 hrs (module totals per syllabus)
01
Introduction to Prompt Engineering
Theory: 4 hrs  ·  Lab/Assignments: 2 hrs  ·  Total: 6 hrs
▼
What is a Prompt: Definitions, Types
Prompt-Response Cycle
Parameters (Temperature, Top-p, Tokens)
Context Windows & Tokenization
Prompt Lifecycle Basics
Lab: Playground Parameter Experiments
Lab: Prompt Debugging Basics
02
Prompt Design Fundamentals
Theory: 4 hrs  ·  Lab/Assignments: 2 hrs  ·  Total: 6 hrs
▼
Clarity, Specificity, Constraints
Instruction Structuring
Role-based Prompting
Response Formatting
Prompt Failures, Anti-patterns
Lab: Rewrite Bad → Good Prompts
Lab: Tone, Style & Persona Tuning
03
Prompt Patterns — Foundations
Theory: 5 hrs  ·  Lab/Assignments: 3 hrs  ·  Total: 8 hrs
▼
Zero-shot prompting
One-shot prompting
Few-shot prompting
Chain-of-thought (CoT)
Iterative refinement / Re-prompting
Multi-turn prompt design
Lab: Create Few-Shot Examples
Lab: CoT vs Non-CoT Evaluation
Lab: Multi-step Prompt Chains
04
LLM Concepts
Theory: 5 hrs  ·  Lab/Assignments: 1 hr  ·  Total: 6 hrs
▼
Evolution of LLMs
Neural Networks Recap
Embeddings & Similarity
Transformer Architecture
Lab: Embedding Visualization
05
API + Structured Outputs
Theory: 4 hrs  ·  Lab/Assignments: 6 hrs  ·  Total: 10 hrs
▼
Playground Exploration
API Parameters
JSON Mode Prompting
Basic Function Calling
Lab: JSON Structured Output Tasks
Lab: Simple Function Call Demo
06
RAG Basics
Theory: 4 hrs  ·  Lab/Assignments: 4 hrs  ·  Total: 8 hrs
▼
Why Retrieval Is Needed
Embeddings Recap
Vector DB Concepts
Lab: Mini-RAG Pipeline
07
Capstone Project
Theory: 2 hrs  ·  Lab/Assignments: 8 hrs  ·  Total: 10 hrs
▼
Project Selection
Prompt Workflow Design
Build Prototype
Review & Presentation
08
Intermediate Prompt Design
Theory: 8 hrs  ·  Lab/Assignments: 10 hrs  ·  Total: 18 hrs
▼
Multi-Instruction & Multi-Constraint Prompting
Designing Prompts for Complex Tasks
Style, Tone, Voice Calibration
Bias-Aware Prompt Structuring
Prompt Failure Analysis Frameworks
Response Shaping & Formatting Patterns
Lab: Rewrite & Optimize Complex Prompts
Lab: Constraint-Based Prompting Workshop
Lab: Prompt Deconstruction & Improvement
Lab: Practical Prompt Debugging
09
Intermediate Prompt Patterns
Theory: 12 hrs  ·  Lab/Assignments: 12 hrs  ·  Total: 24 hrs
▼
Deliberate Prompting
Step-Back Prompting
Least-to-Most Prompting (L2M)
Decomposition Prompting
Socratic Prompting
Self-Critique Prompting
Self-Verification Prompting
Double-Check & Review Prompting
Retrieval-Enhanced Reasoning Prompts
Lab: Compare & Benchmark Patterns
Lab: Build Task-Specific Pattern Sets
Lab: Pattern Mixing & Hybrid Prompting
10
Structured Output Engineering
Theory: 12 hrs  ·  Lab/Assignments: 12 hrs  ·  Total: 24 hrs
▼
JSON Mode Prompting
YAML, XML, Key-Value Output Prompts
Schema-Guided Prompting
Nested Structures & Deep JSON Output
Format Enforcement (Strict Output Control)
Code-Generation Prompting (Python/JS)
DSL Prompting (Domain-Specific Languages)
Lab: Build JSON Schema Prompt Workflows
Lab: Code Generation via Structured Prompts
Lab: Format Validation & Correction Prompts
11
Function Calling for Prompt Engineering
Theory: 6 hrs  ·  Lab/Assignments: 10 hrs  ·  Total: 16 hrs
▼
Function Calling Concepts for Prompt Engineers
Designing Prompts for Function Arguments
Handling Complex Inputs via Prompts
Function-Triggered Output Generation
Multi-Function Selection Prompts
Lab: Simple Function Call Prompting
Lab: Multi-Function Prompt Routing
Lab: Build a Format-Controlled Workflow
12
RAG Prompt Engineering
Theory: 12 hrs  ·  Lab/Assignments: 12 hrs  ·  Total: 24 hrs
▼
Retrieval-Aware Prompt Structuring
Chunking-Aware Prompt Design
Query Rewriting Patterns
Context Injection Prompts
Prompting for Sparse + Dense Retrieval
Prompt Patterns for RAG L2/L3
Lab: Build Retrieval-Aware Prompts
Lab: Optimize Context Use in Prompts
Lab: RAG Prompt Stress Testing
13
Multimodal Prompt Engineering
Theory: 7 hrs  ·  Lab/Assignments: 7 hrs  ·  Total: 14 hrs
▼
Image-to-Text Prompting
Vision Chain-of-Thought
OCR Prompting Patterns
Audio → Text Prompting
Lab: Multimodal Prompt Exercises
14
Intermediate Project
Theory: 2 hrs  ·  Lab/Assignments: 6 hrs  ·  Total: 8 hrs
▼
Project Requirements Definition
Lab: Build Intermediate Project
Lab: Evaluation Using Advanced Patterns
15
Advanced Prompt Patterns
Theory: 15 hrs  ·  Lab/Assignments: 15 hrs  ·  Total: 30 hrs
▼
Advanced Chain-of-Thought Variants
Multi-Persona Prompting
Role-Oriented Reasoning Frameworks
Multi-Step Logical Decomposition
Mixed Prompt Pattern Design
Anthropic-Style Reasoning Prompts
Gemini-Style Reasoning
16
Frontier Reasoning
Theory: 16 hrs  ·  Lab/Assignments: 20 hrs  ·  Total: 36 hrs
▼
Tree-of-Thought (ToT) Prompting
Graph-of-Thought (GoT) Prompting
ReAct Framework (prompt pattern only)
Self-Reflective Prompting
Reflexion-style Prompting
Program-Aided Prompting (PAL techniques)
Meta-Prompting (Prompt that Creates Prompts)
Strategic Prompt Framing
Lab: Build ToT/GoT Prompts
Lab: Reasoning Stress Tests
17
Advanced Structured Outputs
Theory: 10 hrs  ·  Lab/Assignments: 10 hrs  ·  Total: 20 hrs
▼
Deep Schema Design (Recursive & Hierarchical)
Complex Format Enforcement
Advanced Code Generation Prompt Engineering
Multilingual Structured Outputs
Long-Form Structured Output Prompting
Lab: Full Schema + Validation Prompts
Lab: NLP Pipelines via Structured Prompts
18
Advanced RAG Prompt Engineering
Theory: 15 hrs  ·  Lab/Assignments: 15 hrs  ·  Total: 30 hrs
▼
Prompting for Dense + Sparse Hybrid Retrieval
Advanced Query Reformulation Patterns
Multi-hop RAG Prompting
Ranking-aware Prompt Engineering
RAG for Long Documents (Book-Length)
Adaptive Prompting for Retrieval Quality
Lab: Build RAG-L4 Prompt Framework
Lab: Optimize Retrieval Using Prompts
Lab: Evaluate RAG Errors With Prompts
19
Domain Prompt Engineering
Theory: 15 hrs  ·  Lab/Assignments: 15 hrs  ·  Total: 30 hrs
▼
Medical Prompt Engineering
Legal Prompt Engineering
Finance/BFSI Prompt Engineering
HR & Talent Prompt Engineering
Education & Assessment Prompting
Data Analysis Prompt Engineering
Software Engineering Prompt Engineering
Lab: Domain Mini-Projects
20
Safety & Security Prompting
Theory: 10 hrs  ·  Lab/Assignments: 10 hrs  ·  Total: 20 hrs
▼
Prompt-Based Jailbreak Prevention
Harm Prevention Prompt Patterns
Safety-Tuned Prompt Frameworks
Bias Reduction with Prompt Strategies
Security Prompts for Sensitive Data
Lab: Safety Evaluation Drills
Lab: Secure Prompt Redesign
21
PromptOps
Theory: 6 hrs  ·  Lab/Assignments: 10 hrs  ·  Total: 16 hrs
▼
Prompt Versioning Systems
Prompt Testing Frameworks
Linting & Quality Assurance
Governance Policies for Prompt Design
Lab: Prompt Version Control Setup
Lab: Build a Prompt Testing Suite
22
Mega Capstone
Theory: 4 hrs  ·  Lab/Assignments: 20 hrs  ·  Total: 24 hrs
▼
Capstone Planning & Requirements
Architecture of Prompt Workflow
Lab: Build Full Prompt System
Lab: RAG + Structured Output Integration
Lab: Evaluation Framework Integration
Lab: Final Presentation & Defense