Get ready to power up your resume with the GenAI development skills employers need. During this course you’ll explore core prompt engineering strategies—like in-context learning and chain-of-thought—and create and manage robust prompt templates. Plus, you’ll follow best practices to handle common errors and experiment with different LLMs and configurations to strengthen your outputs. You’ll then dive deeper into LangChain, mastering chains, tools, and agents to create smarter, more responsive applications. Through interactive labs, you’ll build a complete generative AI app using Python that accepts user input and processes it through your backend prompt logic. Plus, you’ll explore web-based interfaces using tools like Flask and Gradio, developing real-time user experiences powered by LLMs. By the end, you’ll have the job-ready skills and demonstrable practical experience employers look for to design and implement full-stack GenAI apps that solve real-world problems. Sound good? Enroll today!
DW
Great intro into a bunch of essentials, despite how short the course is. I also liked how some videos were updated to match the ongoing progress in the field
AG
A very good course with lots of hands-on knowledge! Only criticism for authors is that some of the AI models used in exercises have become outdated and are not available in the IBM WatsonX repository.
SS
it was good, but the vid content was just to shallow, while the readings and the other stuffs were amazing!
JB
Some of the labs were broken, they need to fix Llama LLM integration
MS
The context and contents of course . The suggestion will be to add more practical labs of each topics to master it and have capstone project with business use cases.
MR
Good course, but some of the lab elements are not written well and need some experiementation to work.
BF
The course itself is good, informative and well structured. However, the labs need to be updated to the most recent version of Langchain.
RE
Working through the provided lab is the key to getting the most out of this course.
MM
HandsOn Lab is good but the LangChain versions used could have been v0.1.17 or above to learn the latest features.
KP
Overall great course on Gen AI but there are some LangChain code snippets to be modified especially in the guide project because of version changes.
TH
The practice Lab is well designed. Module 3 is somewhat repeated and buggy. But overall, it's nice to join this course
GS
The content covered is very comprehensive and helpful! Would be good if there was less help with the coding tasks, as that would push the learner to memorize things more!
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Mostly all the lessons are AI generated and the explanation looks simply bad.
Videos can be more intuitive for better engaging focus
WORST TEACHINGS HERE, NOT PROPERLY TAUGHT. EXPECTED MUCH BETTER .... I WANTED TO DOWNLOAD ALL THE PDF AND PPT SLIDES SHOWN IN THE VIDEOS. FOR SOME REASON I DONT FIND ANY ??? REALLY WORST TEACHERS HERE , THEY DONT PROVIDE PDF FILES FOR THE PRESENTATION. I WILL NOT RECOMMEND THIS COURSE TO ANYONE
This course provides a clear and practical introduction to building generative AI applications. The examples are well-structured, and the quizzes reinforce the key concepts around models, prompting, LangChain, RAG, and fine-tuning. I especially appreciated how real-world scenarios were used to explain complex ideas in a simple way. A great starting point for anyone looking to get hands-on with generative AI.
IMO, the ungraded app items with the application explanations and examples are excellent learning and practicing tools, which provide the most straightforward way to get to the point and develop proper intuition and knowledge. Personally I spent quite a longer time with them, then the course schedule suggests - and this obviously required longer use of the corresponding shared resources.
Working through the provided lab is the key to getting the most out of this course.
Good introduction - and a smart ad for IBM.
Course is out to date. The theory is still valid, but most of the practical and labs is out to date with libraries and implementation with old version. Would be very helpful if before take the course we know the last programa, course and lesson updates to make a better choice. For example: Some course we can find "Recently updated! February 2026", but most of them we do not know.
The title should be "An intro tutorial in LangChain and Flask with basics on Prompt Engineering". Please get rid of the AI voice. It's really hard to understand and follow. The voice is very "forced" and clips the syllables.
All the lab exercises are outdated. The llms provided by coursera are not compatible with the environment provided in coursera. Even the solution provided by coursera has errors sometimes. Due to this, I had to reprogram multiple parts of the practices, taking longer than the expected time (1h). As a result, I haven't been able to complete the practices as my schedule passed. I'm working while I learn this course and fixing the inconsistencies of the examples provided by the course just consumes too much time for me and I'm not being able to complete everything on time.
Most of the contents are taught by an AI voice, which is very monotonous, same-paced and lacks any immersive experience. This is just a very fast-paced reading by some AI, not teaching something meaningfully. Waste of time!
Most of the content is poor quality, repetitive, surface-level, and entirely AI generated (both the script and audio). Also the lab doesn't work and nobody is responding to people complaining about it on the forums
Worst course I have attended on Coursera. Lot of repetitions, videos of people writing on whiteboards for the most inefficient learning, far too much IBM centred instead general concepts.
Lots of repeat material. Robotic fast paced audio. No pause anywhere. No slides. Incompatible outside of IBM AI framework.
Useless. The potentially interesting parts (vector databases) are simply missing.
El curso me pareció muy completo y bien estructurado, especialmente para entender cómo funcionan los sistemas de IA agéntica modernos. Explica de manera clara los conceptos clave como agentes, flujos de trabajo, orquestación, paralelización, y cómo aplicarlos con frameworks reales como CrewAI, LangGraph, AG2 y BeeAI. Me gustó que no se quedara solo en teoría: muestra buenas prácticas, patrones de diseño, ejemplos de código reales y casos de uso prácticos. BeeAI fue una de las partes más interesantes, porque enseña cómo usar async/await, memoria, prompts dinámicos y salidas estructuradas con Pydantic, cosas que sí se usan en producción. También se valora que comparen diferentes frameworks y expliquen cuándo usar cada uno. En general, el curso da una visión moderna y actualizada de cómo construir sistemas multiagente, y te deja preparado para aplicarlo en proyectos reales. Recomendado si quieres pasar del uso básico de LLMs a construir sistemas inteligentes completos y escalables.
As a Strategic Sales Consultant, I found Develop Generative AI Applications: Get Started highly valuable. The course provides clear insights into generative AI and its practical applications in business, helping me understand how to leverage AI for client solutions, optimize workflows, and enhance sales strategies. It strengthened both my technical knowledge and ability to apply AI-driven strategies for tangible business impact. Highly recommended for sales professionals exploring AI.
I already have ang working experience with this, but this serves as a refresher and deep learning what's happening under the hood esp the concepts and implementation, making it more easier to work with AI using the correct terms, approach and methodoloties. I really appreciate this course especially the last two courses. Awesome!
Great intro course for folks with familiarity of the concepts of AI that starts to cover the interactions with the LLM using tools like FLASK instead of just covering basic concepts like prompting and AI basics. As the first in the series, i am excited to see what the rest of the course has to offer. Highly suggest.
A very good course with lots of hands-on knowledge! Only criticism for authors is that some of the AI models used in exercises have become outdated and are not available in the IBM WatsonX repository.