AI Foundations
Understanding generative AI architectures, prompt mechanics, and safety boundaries
Demystify generative AI and modern LLMs. Understand tokens, embeddings, context windows, and probabilistic text generation. Master structured prompt engineering while learning to detect hallucinations, prevent sensitive data leaks, and navigate ethical AI boundaries.
Course Specifications
Syllabus & Learning Rooms (7)
Progressive sequence of concept briefings, hands-on terminal flag challenges, and a capstone boss assessment.
What are AI, ML and LLMs?
Lesson BriefCore differences between AI, ML, and transformer-based autoregressive language models.
Tokens, context and memory
Lesson BriefTokenization limits, attention mechanisms, and managing the context window.
Hallucinations and overreliance
Lesson BriefWhy LLMs generate convincing falsehoods and how to verify claims.
Prompting fundamentals
Lesson BriefSystem prompts, role framing, few-shot examples, and strict output schemas.
Lab: Pick the best prompt
Hands-on LabEvaluate competing prompts for technical tasks to identify the most robust design.
Using AI safely and responsibly
Lesson BriefPreventing IP exposure, privacy regulations, and responsible enterprise AI policies.
Boss: AI basics
Boss ChallengeDemonstrate comprehensive mastery of generative AI fundamentals and safety.
Skills You Will Master
Every concept is tested against real-world target scenarios, log files, or code repositories.
- LLM transformer mechanics & autoregressive token prediction
- Context window dynamics, attention mechanisms & context limits
- Hallucination detection & citation verification methods
- Structured prompt engineering (role, task, context, schema constraints)
- Data privacy, corporate secret leakage & responsible AI ethics
- Safe evaluation of AI outputs in engineering workflows
Prerequisites
- None - beginners welcome
Associated Career Roles
Devwalls Certified AI Foundations
Upon completing all 7 rooms, you unlock the official proctor-free examination. Score 70% or higher to earn this credential, complete with LinkedIn integration, PDF generation, and public verification ID.
Frequently Asked Questions about AI Foundations
Does this course require math or machine learning prerequisites?
No complex math or model training is required. The focus is on practical architecture understanding, safe operational usage, and prompt design.
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