[100% Off] Learn Features Of Ai : Complete Prompt Engineering Bootcamp

Master Practical Prompt Engineering for ChatGPT, API to Build Smarter AI Workflows and Real-World Applications

Added on October 22, 2025 Development 5 min read

What you’ll learn

• Understand how prompt design influences ChatGPT outputs
• Master key LLM controls (system messages, temperature, top_p, max_tokens, penalties).
• Learn the different types of prompts (instruction, few-shot, chain-of-thought, role, etc.).
• Grasp tokens, cost, and latency trade-offs for efficiency.
• Design, test, and iterate prompts across multiple use-cases (summarization, coding, data extraction, customer support, content generation).
• Build a library of reusable prompt templates.
• Apply chaining methods to connect multiple AI steps into workflows.
• Use tools and APIs (ChatGPT Playground, LangChain, PromptLayer) to automate workflows.
• Measure prompts with qualitative and quantitative metrics (accuracy, F1, BLEU/ROUGE, user satisfaction).
• Run A/B testing to compare prompt variations.
• Optimize for cost and latency in real deployments.
• understand why hallucinations happen and how to mitigate them.
• Implement guardrails (refusal prompts, style constraints, profanity/PII filters).
• Apply legal, privacy, and safety considerations when deploying AI in production.
• Add logging, caching, and observability for scaling.
• Plan failover strategies and human-in-loop safeguards.
• Optimize tokens and examples for efficiency.
• Explore prompt tuning vs. instruction tuning.
• Learn retrieval-augmented generation (RAG) basics.
• Experiment with multimodal prompts (text + image).
• Get an intro to RLHF and future LLM research directions.

Requirements

• Basic computer literacy — comfortable with using web apps, browsers, and online tools.
• Familiarity with ChatGPT (or similar LLMs) — at least basic experience asking questions and reading outputs.
• English proficiency — since prompts and outputs are in English, learners should be able to write clear instructions.
• Introductory programming knowledge (optional but helpful) — understanding JSON, variables, or simple Python/JavaScript will help in API and automation lessons, but not mandatory.
• Curiosity and problem-solving mindset — willingness to experiment, iterate, and think critically about outputs.

Description

Master the practical craft of prompt engineering and learn how to design, test, and deploy reliable AI-driven workflows that power real products. This immersive, hands-on course walks you from first principles to production-ready systems, with a focus on reproducible practices, measurable improvements, and real-world integrations. Whether you want to build smarter content pipelines, automated customer support, or code-generation assistants, this course teaches the exact skills, patterns, and guardrails you’ll use every day as an AI prompt engineering practitioner.

What this course is (straight, no fluff)

Key skills you’ll walk away with

• Expert-level chatgpt prompt engineering techniques: system/user/assistant role design, few-shot teaching, and format enforcement.

• Robust experiment practices: hypothesis design, A/B testing, logging, and quantitative metrics (accuracy, F1 proxies, user satisfaction).

• Production patterns: prompt chaining, map-reduce strategies, validation layers, caching, and failover/human-in-the-loop design.

• Cost & performance optimization: token compression, reuse strategies, and measurable latency/cost tradeoffs.

• Safety & compliance: anti-hallucination patterns, refusal design, PII handling, and legal/privacy considerations.

• Tool integration: how to operationalize prompts via API, Playground, LangChain, and prompt logging/versioning with PromptLayer.

Who this course is for

This course is built for a broad set of learners who want practical impact from AI:

• Product managers and engineers building AI features.

• Content creators and marketers automating workflows with LLMs.

• Support leaders automating first-line responses and triage.

• Entrepreneurs and founders integrating LLMs into SaaS products.

• Data and ML practitioners looking to operationalize LLM prompts
  No prior deep ML knowledge required — but basic familiarity with ChatGPT or similar LLMs and comfort with simple tooling will help you move faster.

Course structure & what we cover (module highlights)

The curriculum is organized into short, focused modules that combine lecture, demo, and hands-on exercises.

• Module 0 — Welcome & Setup: tools, account setup, course roadmap, and how to get the most from the exercises and capstone.

• Module 1 — Fundamentals: LLM behavior, system/user/assistant anatomy, token economics, and live demos that reveal how small prompt changes shift outputs.

• Module 2 — Core Patterns & Templates: instruction clarity, output format enforcement (JSON/CSV), few-shot examples, and reusable template libraries for common tasks.

• Module 3 — Use-Case Deep Dives (Content, Code, Data): real workflows for article generation, unit-testable code generation, and structured data extraction from messy text.

• Module 4 — Evaluation & A/B Testing: practical metrics, experiment design, sampling, and prompt versioning to scale improvements.

• Module 5 — Chaining & Automation: design patterns (map-reduce, critic loops), LangChain demos, building a simple end-to-end pipeline and validators.

• Module 6 — Safety & Hallucinations: why hallucinations happen, grounding strategies, refusal prompts, and legal/privacy guardrails.

• Module 7 — Production Readiness: logging, caching, token optimization, rate limits, monitoring, and disaster recovery patterns.

• Module 8 — Advanced Topics: RAG basics, prompt tuning vs instruction tuning, multimodal prompts, and human-in-the-loop design.

• Module 9 — Capstone: choose from a Customer Support Assistant, Content Studio, or Code Helper — design, prototype, test, and present a deployable prompt-driven project.

Each module contains short lectures, demos, exercises, and downloadable templates you can reuse immediately. The capstone is a project-based assessment where you bring together prompt design, evaluation, and production tooling.

Teaching approach & project-based learning

This course uses an iterative, experiment-driven methodology: for every concept you’ll write a hypothesis, run prompt variants (A/B), log results, and document decisions. The emphasis is on reproducibility — we’ll give you a “prompt lab” template for versioning and metrics so your improvements are measurable and repeatable. Real code snippets, API call examples, and working templates are provided so you can replicate everything in your own environment.

Why this course is different

• Deeply practical: templates, checklists, and production-ready patterns, not just slides.

• Measurement-first: you’ll learn metrics that matter and how to A/B test prompts like product features.

• Safety and compliance integrated: we teach you how to prevent, detect, and mitigate hallucinations and sensitive-data leaks.

• Tool-agnostic but pragmatic: covers ChatGPT & Playground fundamentals while showing how to integrate into LangChain and PromptLayer for scale.

Outcomes — what you will be able to do

By course end you will be able to:

• Design prompts that consistently produce the format and quality you need.

• Implement chatgpt prompt engineering best practices to reduce hallucinations and improve reliability.

• Run structured experiments to measure prompt impact and choose winners.

• Build chained workflows and integrate prompts into APIs and simple orchestrations.

• Optimize prompts for cost and latency while maintaining output quality.

• Apply legal, privacy, and ethical guardrails for real-world deployments.

Practical requirements & resources provided

You’ll need a web-enabled computer, a ChatGPT account (free works for experiments; Plus/Pro recommended for advanced models), and an API key for production exercises. The course includes downloadable PPTs, prompt templates (JSON/CSV), prompt-lab logging templates, code examples for API integration, and a rubric for capstone assessment.