Prompt Engineering Pro Course: Master LLM Prompting from Fundamentals to Production
Learn professional prompt engineering for GPT-4, Claude, Gemini, and Llama. 10 modules, 5 hands-on projects, 40+ hours. Master structured output, chain-of-thought, few-shot, and enterprise prompting patterns.
Course Snapshot
| Course Code | AIM-601 |
| Duration | 40+ hours |
| Modules | 10 |
| Hands-on Projects | 5 |
| Skill Level | Beginner to Advanced |
| Models Covered | GPT-4o, Claude 3.5, Gemini 1.5, Llama 3 |
| Format | Self-paced video + labs + project reviews |
| Certificate | Yes — Prompt Engineering Pro |
| Price | ₹3,999 (Early Bird ₹2,499) |
| Last Updated | July 2026 |
Course Overview
Prompt engineering is the single highest-leverage skill in the AI era. A well-crafted prompt can mean the difference between a model that hallucinates and one that delivers reliable, production-grade output. This course takes you from the fundamentals of how language models interpret text all the way through advanced enterprise patterns used by AI teams at leading companies. You will learn how to design prompts that produce structured JSON, control tone and format, handle multi-turn conversations, and chain reasoning across complex workflows. We cover model-specific techniques for GPT-4o, Claude 3.5, Gemini 1.5, and open-source models like Llama 3, so you are never locked into a single provider. Beyond technique, you will learn evaluation methodologies that let you measure prompt quality objectively, build regression test suites for your prompts, and deploy them with confidence monitoring. By the end of this course, you will have a portfolio of five real-world projects, a personal prompt library you can reuse across any project, and the systematic methodology that senior AI engineers use to tame large language models and make them work reliably for real business problems.
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Enroll Now — Limited Seats →Who This Course Is Built For
This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
Software Developers & Engineers
You write code and want to integrate LLMs into your applications. You need prompts that are reliable, testable, and maintainable in production — not just clever demos.
Product Managers & Founders
You are building AI-powered products and need to understand what good prompting looks like so you can design features, evaluate model output, and communicate effectively with your engineering team.
Data Scientists & Analysts
You work with data and want to add LLM capabilities to your analytical workflows. You need structured output, reliable extraction, and evaluation frameworks to trust model results.
Content Professionals & Marketers
You produce content at scale and want to use LLMs as a force multiplier without sacrificing quality. You need prompts that control tone, style, and format precisely.
This Course Is NOT For You If:
- People who have never used ChatGPT or any LLM — start with a free introductory tutorial first.
- Researchers looking for deep NLP theory or training-from-scratch content — this is an applied prompting course.
- Those wanting a non-technical, no-code overview — we write and test prompts programmatically throughout.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to explain how tokenization, context windows, and temperature affect prompt design and choose optimal settings for any task.
- 2 You will be able to write zero-shot, one-shot, and few-shot prompts that produce consistent, high-quality output across different model families.
- 3 You will be able to design prompts that output valid JSON, XML, Markdown, and custom structured formats with schema enforcement and error recovery.
- 4 You will be able to craft system prompts that define persona, constraints, and behavioral rules for production chatbots and assistants.
- 5 You will be able to implement advanced patterns including chain-of-thought, self-consistency, tree-of-thought, ReAct, and prompt chaining.
- 6 You will be able to adapt prompts for GPT-4o, Claude 3.5, Gemini 1.5, and Llama 3, understanding each model's unique strengths and quirks.
- 7 You will be able to build domain-specific prompts for customer support, code generation, data extraction, summarization, and creative writing.
- 8 You will be able to evaluate prompts using LLM-as-judge, human evaluation frameworks, and automated regression test suites.
- 9 You will be able to manage prompt versions, deploy them with A/B testing, and monitor production performance with observability tooling.
- 10 You will be able to ship a complete prompt-engineering project from requirements to deployed, monitored production system.
Download Free Course Syllabus
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Download Free Syllabus →Complete Course Curriculum
10 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — Foundations of Prompt Engineering
3 hours- Tokenization and how models see your text as numerical tokens, not words.
- Context windows explained: input limits, output limits, and what happens when you exceed them.
- The role of temperature, top-p, and frequency penalty in controlling output creativity and determinism.
- The four pillars: instruction, context, input data, and output format.
- Why clarity beats cleverness and how ambiguity causes hallucination.
- Common anti-patterns: vague instructions, conflicting constraints, and overloaded prompts.
- Building a structured prompt template with variables and placeholders.
- Testing your prompt across multiple inputs and iterating based on failures.
- Setting up your development environment with the OpenAI, Anthropic, and Google SDKs.
- When prompting is enough and when you need fine-tuning or RAG.
- Cost, latency, and quality trade-offs between approaches.
- The modern stack: prompting as the default, fine-tuning as the exception.
MODULE 2 — Core Prompting Techniques
4 hours- Getting models to perform tasks with no examples using clear instructions alone.
- When zero-shot works well and when it fails catastrophically.
- Techniques for improving zero-shot reliability through precise language.
- Providing examples in the prompt to guide model output format and style.
- How many examples to include and how to select the most effective ones.
- Example ordering effects and why the last example matters most.
- How instruction-tuned models differ from base models and what it means for prompting.
- Using system, user, and assistant message roles effectively in chat completions.
- Converting legacy text-completion prompts to modern chat-format prompts.
- Techniques for enforcing concise, verbose, or exact-length responses.
- Using delimiters like XML tags, Markdown headers, and special tokens to structure output.
- Preventing prompt drift in long conversations with format reinforcement.
MODULE 3 — Structured Output Generation
4 hours- Designing prompts that reliably produce valid JSON with nested structures.
- Using OpenAI structured outputs, Anthropic tool use, and Gemini response schemas.
- Handling edge cases: empty fields, arrays, nested objects, and nullable values.
- Defining output schemas in Python with Pydantic and in JavaScript with Zod.
- Automatic retry logic when model output fails schema validation.
- Building a reusable structured-output pipeline with validation and error recovery.
- When to choose JSON vs XML vs Markdown based on your downstream processing needs.
- Prompting for tabular data in CSV and Markdown table formats.
- Handling escaping, special characters, and multi-line content in structured output.
- Robust parsing strategies for imperfect model output using regex and AST parsers.
- Self-correction prompts: asking the model to fix its own malformed output.
- Building a fallback chain: structured output, then repair prompt, then human review.
MODULE 4 — System Prompts and Persona Design
3 hours- The system prompt as the constitution: defining rules the model must follow in every turn.
- Structuring system prompts with role, capabilities, constraints, and output rules.
- Testing system prompts for compliance and edge-case violations.
- Creating consistent AI personas with defined voice, vocabulary, and behavioral boundaries.
- Techniques for matching tone to brand guidelines and audience expectations.
- Preventing persona drift in long conversations and multi-session interactions.
- Writing constraints that models actually follow: positive framing, prioritization, and redundancy.
- Handling conflicting constraints and building a constraint hierarchy.
- Using pre-flight checks and post-generation validation to enforce rules.
- Managing context window across long conversations with summarization and pruning.
- Designing prompts that reference previous turns without repeating full history.
- Building conversation state machines with conditional prompt selection.
MODULE 5 — Advanced Prompting Patterns
5 hours- Triggering step-by-step reasoning with "Let's think step by step" and structured reasoning blocks.
- When chain-of-thought improves accuracy and when it wastes tokens.
- Controlling reasoning depth and hiding reasoning from end users.
- Generating multiple responses and selecting by majority vote or confidence scoring.
- Trading latency and cost for accuracy in high-stakes reasoning tasks.
- Implementing self-consistency with temperature variation and prompt paraphrasing.
- Exploring multiple reasoning paths in parallel and pruning weak branches.
- Implementing tree-of-thought with a controller prompt that evaluates and selects branches.
- When graph-of-thought outperforms linear chain-of-thought for complex problems.
- The ReAct pattern: interleaving reasoning steps with tool calls and observations.
- Building a ReAct agent that can search, calculate, and call APIs within a single prompt chain.
- Handling tool failures, retries, and recovery within the ReAct loop.
- Breaking complex tasks into sequential prompts where each output feeds the next.
- Parallel prompting: running independent prompts concurrently and merging results.
- Building a prompt orchestration layer with routing, branching, and fallback logic.
MODULE 6 — Model-Specific Prompting
4 hours- GPT-4o-specific features: structured outputs, function calling, and vision integration.
- Optimizing for GPT-4o's strengths in code generation and multi-language support.
- Token economics: choosing between GPT-4o, GPT-4o-mini, and o1 based on task complexity.
- Claude's XML-tag prompting style and why it produces cleaner structured output.
- Using Claude's extended thinking mode for complex multi-step reasoning.
- Leveraging Claude's larger context window for document-heavy prompts and long conversations.
- Gemini's multimodal prompting: combining text, images, video, and audio in a single prompt.
- Using Gemini's response schema API for guaranteed structured output.
- Optimizing for Gemini's massive context window with long-document summarization and retrieval.
- Adapting prompting techniques for models without instruction tuning or with weaker instruction following.
- Using chat templates and special tokens correctly with Hugging Face Transformers.
- Performance and quality differences between open-source and proprietary models for the same prompt.
MODULE 7 — Domain-Specific Prompting
4 hours- Designing prompts for support chatbots that resolve tickets without human escalation.
- Building a knowledge-grounded support prompt with retrieval-augmented context injection.
- Handling escalation, sentiment detection, and angry-customer scenarios in prompts.
- Prompting for production-quality code with tests, error handling, and documentation.
- Using system prompts to enforce coding standards, language version, and framework conventions.
- Building a code review assistant that catches bugs, security issues, and style violations.
- Extracting structured data from invoices, contracts, and forms with high accuracy.
- Handling messy real-world documents: OCR errors, inconsistent formatting, and missing fields.
- Building extraction pipelines with validation, confidence scoring, and human-in-the-loop review.
- Controlling summary length, style, and focus with parameterized prompt templates.
- Generating on-brand marketing copy with tone, voice, and style transfer techniques.
- Building a content generation pipeline with ideation, drafting, editing, and fact-checking prompts.
MODULE 8 — Prompt Evaluation and Testing
4 hours- Defining what "good" means: accuracy, relevance, coherence, faithfulness, and helpfulness.
- Building a scoring rubric and converting subjective quality into measurable metrics.
- Using established benchmarks: BLEU, ROUGE, BERTScore, and their limitations for prompt evaluation.
- Using a strong model to evaluate outputs from your production prompts automatically.
- Designing judge prompts that are fair, consistent, and resistant to position bias.
- Calibrating LLM judges against human evaluations and handling disagreement.
- Setting up structured human evaluation with blind comparison, Likert scales, and pairwise ranking.
- Building an annotation pipeline with inter-annotator agreement and quality control.
- Using human feedback to improve prompts through targeted few-shot example selection.
- Building a test suite of inputs and expected outputs for your prompts.
- Automating prompt regression tests in CI/CD to catch quality drops before deployment.
- Tracking prompt quality over time with dashboards and alerting on metric degradation.
MODULE 9 — Enterprise Prompt Engineering
4 hours- Treating prompts as code: version control, code review, and rollback strategies.
- Using prompt management platforms like LangSmith, PromptLayer, and Helicone.
- Building a prompt registry with metadata, ownership, and lifecycle management.
- Designing statistically sound A/B tests for prompt variations in production.
- Measuring business metrics, not just quality metrics: conversion, retention, and cost.
- Building an experimentation platform with automatic winner selection and traffic allocation.
- Logging every prompt, response, and metadata for audit and debugging.
- Detecting prompt injection attacks, jailbreaks, and anomalous usage patterns.
- Setting up alerts for latency spikes, cost overruns, and quality degradation.
- Preventing prompt injection: input sanitization, system prompt protection, and output filtering.
- Handling PII and sensitive data in prompts with redaction and tokenization.
- Meeting compliance requirements: GDPR, HIPAA, and SOC 2 for AI-powered applications.
MODULE 10 — Capstone Projects
5 hours- Build a system that extracts structured data from PDFs, images, and emails using model-specific prompts.
- Implement schema validation, error recovery, and confidence scoring.
- Deploy with a REST API and monitor extraction accuracy in production.
- Design a multi-turn support chatbot with persona, knowledge grounding, and escalation logic.
- Build a regression test suite and LLM-as-judge evaluation pipeline.
- Implement conversation memory, summarization, and context window management.
- Create a prompt pipeline that reviews code for bugs, security issues, and style violations.
- Generate documentation, test cases, and refactoring suggestions from code input.
- Build a CLI tool and IDE integration for developer workflows.
- Build a ReAct agent that reasons, calls APIs, and takes actions to complete complex tasks.
- Implement tool selection, error handling, and multi-step planning.
- Add observability with step-by-step logging and human approval gates.
- Build a prompt registry with versioning, A/B testing, and deployment pipelines.
- Implement automated evaluation with LLM-as-judge and regression test suites.
- Create a dashboard for monitoring prompt quality, cost, and latency in production.
Real-World Projects & Portfolio Outcomes
5 portfolio-worthy projects that prove your skills to employers.
Multi-Format Document Processor
Build a production system that extracts structured data from PDFs, images, and emails using model-specific prompts with schema validation and error recovery.
Production Customer Support Agent
Design and deploy a multi-turn support chatbot with persona design, knowledge grounding, escalation logic, and a full evaluation pipeline.
Code Review and Documentation Assistant
Create a prompt pipeline that reviews code for bugs and security issues, generates documentation, and suggests refactoring — packaged as a CLI tool.
ReAct Agent with Tool Use
Build a ReAct-pattern agent that reasons through complex tasks, calls external APIs, handles errors, and produces structured output with full observability.
Enterprise Prompt Management Platform
Build a prompt registry with version control, A/B testing, automated evaluation, and a monitoring dashboard for production prompt management.
Tools, Technologies & Models Covered
LLM Models Covered
| GPT-4o | OpenAI | Structured output, function calling, vision, code generation |
|---|---|---|
| GPT-4o mini | OpenAI | Cost-effective tasks, high-volume processing, classification |
| Claude 3.5 Sonnet | Anthropic | XML-tag prompting, extended thinking, long-context reasoning |
| Claude 3 Opus | Anthropic | Complex reasoning, nuanced writing, multi-step analysis |
| Gemini 1.5 Pro | Multimodal prompting, massive context, response schemas | |
| Gemini 1.5 Flash | Fast multimodal tasks, high-volume classification | |
| Llama 3.1 70B | Meta / open-source | Open-source prompting, chat templates, self-hosted deployment |
| Mistral Large | Mistral AI | European deployment, function calling, structured output |
Tools & Frameworks Covered
| OpenAI Python/Node SDK | Direct API access for GPT models | Deep — all features and parameters |
|---|---|---|
| Anthropic SDK | Direct API access for Claude models | Deep — XML prompting, extended thinking |
| Google AI SDK | Direct API access for Gemini models | Deep — multimodal, response schemas |
| LangChain | Prompt templates, chains, and output parsers | Intermediate — prompt orchestration patterns |
| Instructor | Structured output with Pydantic/Zod validation | Deep — schema enforcement and retry logic |
| Promptfoo | Prompt evaluation and regression testing | Intermediate — test suites and comparison |
| LangSmith | Prompt management, tracing, and observability | Intermediate — production monitoring |
| PromptLayer | Prompt versioning and management | Intermediate — registry and lifecycle |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Dr. Arjun Mehta
Principal AI Engineer & Former Lead Prompt Engineer at a top AI startup
8+ years in NLP and applied AI, 4+ years specifically in LLM prompt engineering
Shipped 20+ production LLM applications including support agents, code assistants, and document processors
Trained 5,000+ engineers in prompt engineering through workshops and courses
Prompt engineering is software engineering for natural language. Treat prompts with the same rigor you treat code: version them, test them, review them, and monitor them in production.
Course Team & Curriculum Design
This course is co-developed with a team of practicing AI engineers who work with LLMs daily at companies ranging from early-stage startups to Fortune 500 enterprises. Each module is reviewed by a domain expert — our evaluation module is co-authored by an ML research scientist, our enterprise module by a platform engineering lead, and our domain-specific modules by practitioners who have shipped products in each area.
Learning Path & Prerequisites
Prerequisites
- Basic programming knowledge in Python or JavaScript — you should be comfortable writing functions and using APIs.
- Familiarity with using ChatGPT, Claude, or any LLM — you have experimented with prompts casually.
- Understanding of basic data formats: JSON, CSV, and Markdown.
- No machine learning background required — this course is applied, not theoretical.
Recommended Learning Paths
Complete Beginner to Prompt Engineering Professional
- Start with Module 1 to understand how models interpret prompts and set up your environment.
- Complete Modules 2-4 to master core techniques, structured output, and system prompt design.
- Work through Modules 5-7 for advanced patterns, model-specific techniques, and domain applications.
- Take Module 8 to learn evaluation so you can measure and prove prompt quality.
- Finish with Modules 9-10 for enterprise skills and capstone projects.
- Build your prompt library and portfolio throughout — every module includes exercises.
Developer Integrating LLMs into Applications
- Skim Module 1 if you are new to tokenization and context windows.
- Focus on Modules 3 and 4 for structured output and system prompts — these are your daily drivers.
- Study Module 5 for ReAct and prompt chaining to build complex agent workflows.
- Use Module 6 to optimize for your specific model provider.
- Jump to Module 9 for production deployment, monitoring, and security.
- Complete the capstone projects that match your use case.
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Early Bird | ₹2,499 | Full course access, 5 project reviews, community access, certificate — limited time offer |
|---|---|---|
| Standard | ₹3,999 | Full course access, 5 project reviews, community access, certificate, 3 months instructor Q&A |
| Premium | ₹6,999 | Everything in Standard plus 1-on-1 mentorship, career guidance, lifetime updates, priority support |
What Is Included
- 40+ hours of HD video lessons with downloadable transcripts
- 10 modules with hands-on labs and exercises
- 5 capstone projects with personalized feedback
- Access to our private community of AI engineers
- Downloadable prompt library with 100+ tested prompts
- Certificate of completion — Prompt Engineering Pro
- 3 months of instructor Q&A access (Standard and Premium)
- Lifetime course updates as models and techniques evolve
30-day money-back guarantee. If you complete the first 3 modules and do not find the course valuable, we will refund your full payment — no questions asked.
Frequently Asked Questions
Do I need to know how to train or fine-tune models?
No. This course is purely about prompting — getting the best possible output from existing models. We cover when fine-tuning is appropriate, but the focus is on prompt design, not model training.
Which programming language do we use?
We provide examples in both Python and JavaScript. You only need one. Most students use Python, but JavaScript examples are provided for every exercise.
Do I need paid API access to GPT-4 or Claude?
You will need API access to at least one model provider (OpenAI, Anthropic, or Google). Budget approximately ₹500-1,000 for API credits during the course. We also show how to use free tiers and open-source models for practice.
How long does the course take to complete?
The course is 40+ hours of content. Most students complete it in 6-8 weeks at 5-7 hours per week. You have lifetime access, so you can go at your own pace.
Are the prompts I learn transferable across different models?
Yes. We teach universal principles that apply to all models, plus model-specific optimizations. You will learn to adapt any prompt to any model — that adaptability is a core course outcome.
Is this course updated when new models come out?
Yes. We update the course within 30 days of major model releases. The last update was July 2026, covering GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. Lifetime updates are included.
Will this help me get a job in AI?
Prompt engineering is one of the most in-demand AI skills. The course includes portfolio projects, a certificate, and career guidance (Premium tier) to help you position yourself for AI engineering roles.
What if I get stuck on a project?
Every project includes detailed walkthroughs, and you can ask questions in the community forum. Standard and Premium tiers include direct instructor Q&A access.
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