CourseVerdict

ChatGPT Prompt Engineering for Developers vs AI For Everyone

Same Bayesian formula, same rubric — so the difference in scores reflects the difference in the courses, not the difference in how we evaluated them.

DeepLearning.AI (with OpenAI) · AI & ML Courses

ChatGPT Prompt Engineering for Developers

4.4/ 5 · 44 opinions
33 positive8 neutral3 negative/ 44 total

DeepLearning.AI (Coursera) · AI & ML Courses

AI For Everyone

4.0/ 5 · 52 opinions
38 positive9 neutral5 negative/ 52 total

Per-criterion

Content quality4.3 / 5

Two core principles (write clear and specific instructions, give the model time to think) plus modules on iterative prompt development, summarizing, inferring, transforming, expanding, and building a chatbot. Reviewers praise the clarity and the runnable Jupyter notebooks. The honest limit is depth: it was built in April 2023 on GPT-3.5 Turbo and does not cover newer patterns like tool calling, structured outputs, or reasoning models.

Instructor4.8 / 5

Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) are about as authoritative as the field gets. The teacher-student dynamic — Ng asking the clarifying questions a beginner would ask while Fulford demonstrates — is repeatedly cited as a strength that mirrors how learners actually think.

Value for money5.0 / 5

Free on the DeepLearning.AI platform with every code example runnable in-browser, no API key or local setup required. Reviewers consistently call out "the best part is that it's free" as a decisive advantage over the paid prompt-engineering courses that flooded the market in 2023.

Support3.3 / 5

Being a one-hour self-paced short course, there is no graded assignment, cohort, or mentor support. The OpenAI and DeepLearning.AI community forums are active and useful, but learners are largely on their own. For a course this short the need is limited, but there is no structured help.

Real-world use4.2 / 5

Six practical use cases implemented end-to-end give learners patterns they can apply the same day. Developers report it directly improved their ability to build LLM features. The caveat is that the API-level patterns are a foundation, not a production blueprint — several reviewers wanted more on structuring LLMs into real applications.

Content quality3.8 / 5

Four weeks of AI fundamentals — project workflow, business strategy, ethics and societal impact. Pre-dates the generative AI era; reviewers consistently note the absence of LLMs, ChatGPT, and prompt engineering as a meaningful gap for 2024+ learners.

Instructor4.8 / 5

Andrew Ng is the most cited strength across every review source. Reviewers praise his ability to make complex ideas feel intuitive without equations. His real-world case studies and calm, clear delivery are mentioned in the majority of positive reviews.

Value for money4.9 / 5

Free to audit on Coursera — all video lectures and readings are accessible at no cost. Certificate requires a paid subscription (~$49/month). Most reviewers recommend auditing free; the certificate has limited standalone career value.

Support3.2 / 5

Coursera discussion forums are present but described as low-activity for this course. There is no hands-on project work, so the need for support is limited. DeepLearning.AI community forums exist but are not regularly referenced in learner reviews of this specific course.

Real-world use3.5 / 5

Reviewers praise the AI Transformation Playbook and project workflow frameworks as genuinely useful for managers. The honest limit is the lack of hands-on practice — learners finish with vocabulary and strategy but no portfolio artefacts or technical skills to demonstrate.

Scoring methodology applies identically to every course on the site — see the formula.