BROWSE TORRENTS

Udemy - Learn Features of AI - Complete Prompt Engineering Bootca...

Category: Other
Type: Tutorials
Language: English
Total Size: 342.6 MB
Uploaded By: freecoursewb
Downloads: 42955
Last checked: Nov. 14th '25
Date uploaded: Nov. 14th '25
Seeders: 19943
Leechers: 10237
MAGNET DOWNLOAD
INFO HASH: 9790D8C491D7F7481B6C0CA1FC21D9AEC65EADDD

Learn Features of AI : Complete Prompt Engineering Bootcamp

https://WebToolTip.com

Last updated 10/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: | Size: 343 MB

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

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.