Master Practical Prompt Engineering for ChatGPT, API to Build Smarter AI Workflows and Real-World Applications
What you will 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.
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- Understand the intricate “psychology” of LLMs, learning to speak their language for superior output quality and more precise control over AI behavior.
- Transform from an AI user to an AI architect and strategist, capable of designing, optimizing, and guiding sophisticated AI implementations within any organization.
- Develop a critical eye for AI output quality, mastering the art of debugging, root cause analysis, and iterative refinement to consistently achieve desired results.
- Gain the strategic ability to bridge complex business objectives with practical AI capabilities, translating abstract needs into actionable and effective prompt designs.
- Learn to innovate entirely new product features and business models by creatively leveraging generative AI, unlocking unprecedented opportunities for growth.
- Master the skill of orchestrating multi-AI workflows, seamlessly integrating diverse models and external data sources to build advanced, intelligent applications.
- Cultivate a strong foundational understanding in ethical AI development and deployment, ensuring responsible use, bias mitigation, and positive societal impact.
- Become adept at future-proofing your AI skills, understanding underlying principles that transcend specific models and adapt to the rapidly evolving LLM technologies.
- Position yourself as a key contributor in AI-driven teams, capable of optimizing AI performance, managing project lifecycles, and fostering collaborative innovation.
- Unlock the capacity to personalize AI experiences at scale, crafting dynamic and adaptive responses tailored to individual user preferences and contexts.
- Acquire the expertise to demystify common AI failures, implementing robust strategies to prevent hallucinations, biases, and other suboptimal outputs.
- Establish best practices for collaborative prompt development and management, fostering efficient teamwork and shared knowledge bases for scalable AI projects.
- PROS:
- Immediate real-world applicability: Gain highly sought-after, hands-on skills to design, deploy, and manage AI solutions in professional settings, significantly boosting your market value.
- Strategic AI leadership: Move beyond basic usage to architect, optimize, and strategically manage complex AI workflows, positioning you as an indispensable AI driver.
- Future-proofed learning: While practical, the course grounds you in core prompt engineering principles that transcend specific models, preparing you for future AI advancements.
- Ethical and responsible AI development: Learn to build and deploy AI systems with a critical understanding of their societal impact, ensuring thoughtful and safe implementation.
- CONS:
- Demands continuous self-education: The rapidly evolving AI landscape means ongoing learning and adaptation beyond the course material are essential to maintain cutting-edge expertise.
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