Master Prompt Engineering With Industry Experts
Develop in-demand Prompt Engineering skills through hands-on projects, real-world AI applications, and expert-led training. Learn advanced prompting techniques for ChatGPT, Gemini, Claude, and other LLMs to build AI-powered solutions, automate workflows, and boost productivity. Complete the program with globally recognized certifications and comprehensive placement support to kickstart your AI career.
Choose Your Learning Path
Flexible Plans Designed to Fit Your Schedule & Learning Style
Select the learning mode that works best for you — self-paced, live online, or in-person classroom training
Blended Learning
Self Learning + Live Mentoring
- Self Learning + Live Mentoring
- NASSCOM & IBM & AAAI Certification
- 1 Year Access to E-learning
- 25 Capstone & 1 Client Project
- Job Assistance
- 24x7 Learner Assistance
Live Virtual
Instructor Led Live Online
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 48 -Hour Live Online Training
- 25 Capstone & 1 Client Project
- 365 Days Flexi Pass + Cloud Lab
- Placement Assurance
Classroom
In-Person Classroom Training
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 48 -Hour Live Online Training
- 25 Capstone & 1 Client Project
- Cloud Lab Access
- Placement Assurance
Tools & Technologies
Master Industry Tools
Hands-on training on the tools and technologies used by top data teams
worldwide.
Certification
NASSCOM & IBM & AAAI Certification
Flexible Financing Options
Pay in Easy Installments at 0% Interest
We’re dedicated to making our programs accessible. No hidden costs, transparent pricing
EMI Available
ShopSe
No Cost EMI & Scholarships Available
Our Partners
Companies That Hire Our Graduates
200+ leading companies trust Data Sprint graduates to drive their data
initiatives.
Why Data Sprint
Why Thousands Choose Data Sprint
We’re committed to transforming careers with quality education and real-
world experience.
Course Syllabus
Comprehensive Prompt Engineering Curriculum
A practical, hands-on curriculum covering the full spectrum of prompt engineering — from foundational concepts to advanced agentic and production-grade techniques.
Foundations of Prompting
• How LLMs generate text (tokens, temperature, sampling)
• Context windows and attention mechanisms
• System vs user vs assistant roles
• Instruction following vs completion models
• Model families: GPT, Claude, Gemini, Llama
• Prompt anatomy: task, context, format, constraints
Instruction Clarity & Formatting
• Positive vs negative instructions
• Role prompting and persona assignment
• Output format control (JSON, XML, Markdown)
• Delimiters and structured sections
• Length and tone calibration
• Iterative prompt refinement workflow
Few-Shot & In-Context Learning
• Zero-shot, one-shot, few-shot mechanics
• Selecting and ordering effective examples
• Label formatting and demonstration quality
• Contrastive few-shot (positive + negative examples)
• Dynamic example selection strategies
• When few-shot hurts vs helps
Reasoning & Chain-of-Thought Prompting
• Chain-of-Thought (CoT) fundamentals
• Zero-shot CoT: ‘think step by step’
• Least-to-most prompting
• Tree-of-Thoughts (ToT) for branching problems
• Self-consistency and majority voting
• Program-aided language models (PAL)
RAG Prompting & Context Management
• Prompt templates for RAG pipelines
• Grounding and citation prompting
• Handling long and noisy context windows
• Lost-in-the-middle mitigation strategies
• Conditional prompting based on retrieved content
• Hypothetical document prompting (HyDE)
Agentic & Tool-Use Prompting
• ReAct (Reasoning + Acting) prompting pattern
• Tool/function calling prompt design
• System prompts for autonomous agents
• Agent memory and state injection
• Prompt chaining and workflow orchestration
• Multi-agent communication protocols
• MCP-aware prompt design
Adversarial Prompting & Safety
• Prompt injection and hijacking attacks
• Jailbreaking taxonomy and techniques
• Indirect prompt injection in RAG systems
• Defense strategies: input sanitization, output validation
• Constitutional AI and self-critique prompting
• Guardrail design for production systems
• Red-teaming methodology
Prompt Evaluation & Production Ops
• LLM-as-judge evaluation frameworks
• Automated evals: RAGAS, DeepEval, PromptFoo
• Prompt versioning and regression testing
• A/B testing prompts in production
• Cost vs quality trade-off analysis
• Prompt management in CI/CD pipelines
• Latency optimization strategies
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