Google Gemini AI Reviews

I have used that recently to create test cases for my scenarios. I have prompt engineering skills, so it is easy for me to get the right response which I use in my project.

I have used it for creating my test cases. I gave the prompt about how I could have the steps as I was new to the tool at that time and was not knowing how much time it would take for me. I got the response indicating the time required to learn and apply the skills. When I used the prompt to create test cases and added them to the project, I received good feedback indicating I had covered all positive and negative scenarios.

Most of the inputs are acceptable. It will give the correct response, but we need to have appropriate skills. For example, if we are not sure about our question or what desired output we need, if it is not a valid question, we will not receive the correct solution. The process requires natural language processing skills and knowledge for all prompts.

I use ChatGPT on a daily basis. It is available on my phone and I use it for basic daily chores such as food recipes and other tasks, apart from technical work. I have also used ChatGPT to create test cases. I have compared responses from Gemini and ChatGPT and received similar results but presented differently. Every tool has its uniqueness. It is good, and I am enjoying using both tools, but most often I use ChatGPT because I haven't used Gemini recently.

In initial stages, when using without prior experience, we must have basic understanding and prompt engineering skills. It will work for others even if they do not have skills from an IT professional perspective. Normal people can use it to improve their English or grammar. They can give prompts and expect sentences without grammatical mistakes. This way it is very helpful for normal people as well.

I have completed certification on Copilot, ChatGPT, and prompt engineering. I understand how we can use AI tools for daily work and testing progress. I have understanding of these GenAI products.

The main advantages are the test cases and scenarios covered from a user perspective. It also provides a scheduled timeline for solutions. If we work according to that, it takes approximately that time. We need to have a good understanding of the prompts we are using to get valid results. I would try and explore more options in Gemini in the future.

According to my understanding, if we provide a valid and proper prompt and seek a response, it will definitely give valid and helpful responses 95% of the time if we use it correctly.

I was thinking that now it is creating advanced images. In the same way, we could get video versions easily so it would be very helpful for people interested in video editing. If we give the scenario, it should try to create a story in video edition. I have not used that if it is already available.

I used it six months ago.

I didn't face any issues, but if we have not signed in to Gemini and other AI apps, it will not record our history and will give random data. When we sign up and use it daily, it collects our history and gives perfect responses suitable for our point of view. If we have the sign-in option, it will definitely give more correct answers instead of using it in online mode without signing in.

It is scalable.

I have used different solutions. Currently, I am using Tosca. Previously, I worked on LEAPWORK for more than two years. For the past three months, I have been using Tosca automation tool.

I have not tried that yet.

I haven't used it to that extent.

I have not explored that yet.

I have not used the advanced version yet. I have explored what was available for me in the free versions.

I use ChatGPT more often, but in some situations, I have used Gemini and gotten good results for my use cases. I have also used the suggestions which I received from it and it has given me positive responses.

I have basic understanding of this.

It is a good tool. Even beginners can use it. For IT professionals, if we get dedicated code, there should be a version specifically for IT professionals who can give input and get desired output for coding parts.

Users can definitely try the free version which is available for everyone. If they want more privacy and advanced features, they can pay and try that as well to get more detailed answers. The free version is good, but the main consideration is using our prompts. We cannot use any coding in online mode due to privacy concerns. I prefer using the advanced paid version which has more privacy for all IT-related work.

I do not have much knowledge about machine learning algorithms as I have not recently worked with them.

I worked for more than six months on that in my previous project.

Review Rating: 8/10

My use cases for Google Gemini are largely towards market research where I generate reports and understand the market, and there is a clear differentiator with Google Gemini that wasn't there earlier with other LLMs, specifically that it does a better job with real-time searches of content because it has access to Google's search results.

I use it for creating market research reports, planning strategies, and analyzing spreadsheets since my role involves a lot of market strategy and planning how to enter a new market.

When it comes to integrating Google Gemini with other Google products, it integrates well, including on Google Sheets where you can ask questions. I think it's available there, but I'm not sure about Word. Using it on the spreadsheets integrates seamlessly because they have the Google Suite available.

Regarding the real-time data streaming analysis tools of Google Gemini, I find them effective, as these are foundational LLMs fed with data sets every day, and I tend to shuffle between three or four different LLMs, including Google Gemini. From my experience, Google Gemini stands out for real-time data analysis and factual information.

The best features for my use cases with Google Gemini are its dynamic LLM model that can perform dynamic searches, which is wonderful for factual searches to understand numbers and to generate real-time answers.

I find that Google Gemini does a really good job and that's when I use it to get factual real-time answers.

Google Gemini's biggest strength is also its drawback; it's excellent for generating reports and working with data in real-time, but it isn't the most creative LLM for tasks such as creating a digital storytelling campaign or crafting marketing messages.

Google Gemini's biggest strength is also its drawback; it's excellent for generating reports and working with data in real-time, but it isn't the most creative LLM for tasks such as creating a digital storytelling campaign or crafting marketing messages.

I have been using Google Gemini directly for almost three years now since it came out.

For my use case, stability is good; I don't notice any glitches, and it's quite stable overall.

In terms of scalability for Google Gemini, the paid tiers offer better access to more computing power, which is where my use case hits the limits, but this would be a better question for someone developing AI applications.

I have never contacted technical support from Google regarding Google Gemini because I've never had the need for it.

Positive

Since I consume Google Gemini on the cloud, there's no setup involved; you just go and start using it.

When comparing Google Gemini with Claude, Llama, and GPTs, each has its benefits based on use cases. Since I'm not a developer, I don't use them for creating code bases beyond some HTML or CSS scripting. I understand that different LLMs serve different purposes, and I feel Google Gemini excels for reporting and analysis.

I currently work at Oracle, so most of the tools we use are in-house and we don't use many third-party tools. However, one tool which comes into use quite a bit for us is Seismic, which I just started using.

Seismic is a sales enablement tool that integrates with Salesforce, and as I am on the marketing side of things, we use this tool for providing enablement collateral for sales enablement. Personally, I use Figma as an open-source tool. After AI has come in, I largely rely on AI for most things and we have Oracle's own copilot.

As an Oracle employee, I don't directly work with any Google solutions for AI or Microsoft, but we have Oracle's own AI interface that takes APIs from different LLM models, so we consume ChatGPT, Google Gemini, and Cohere through Oracle's interface built for us.

I have not used scalable ML models, as I don't directly deal with the engineering side of things. I have not used Google Gemini on-premises; everything I've used has been through existing Google servers. For the pricing of Google Gemini, you can find it online; I've not used the paid version, but I know ChatGPT's paid versions aren't very expensive and I find the pricing model satisfactory for users.

I don't use Google Gemini through AWS Marketplace as I'm an Oracle employee, so we use it through OCI, which is Oracle Cloud Infrastructure, a competitor of AWS.

My rating for Google Gemini is 8 out of 10.

Public Cloud

Google

We have implemented real-time data streaming and analysis tools in Gemini. We have used them to develop complicated systems such as a tender valuation system and a vision tool to classify images on real-time data. The performance is good.

The best features in Gemini are quite good. It is at the top of the chart as far as the models are concerned. It is good through API and they offer their services. The response of the model is good both for vision as well as for the text, which is the large language model.

The integration of Gemini with other Google services is quite good. We develop the application using open source platforms such as LangGraph or LangChain, where the integration for Gemini is quite good. While we don't use other Google services such as cloud, the integration appears to be good. The only drawback for Gemini services or Google Vertex AI platform is that their platform understanding and getting started with the platform is a difficult process.

Gemini's deployment flexibility, both cloud and on-premises, primarily benefits my organization by being in the cloud. It works through AWS cloud and the integration is easy.

Gemini can be improved in several ways. The ChatGPT platform is much more simple for getting started, whereas Gemini's initial setup is more difficult, particularly when using other services such as Document AI service. For Document AI services, there is a long drawn process involving installing the Google command-line interface, downloading the credentials, and configuring application access to those credentials. Microsoft Azure works through API calls, which means you download an API token, store it in your program, and start using it. With Gemini, it's a more tedious process. Though the facilities offered in Google Cloud Platform are extensive, one has to make a rigorous initial effort to get all of those things started.

I have experience with Gemini since it was launched when version 2.5 was released approximately three to four months ago. It is a multi-modal model that can be used for both image and text processing.

Gemini is a stable solution. Any AI solution using a large language model is probabilistic. Whatever it generates is basically probabilistic text. One has to iterate and work out a solution to ensure stability. It is stable, but the developer has to make it stable.

Gemini is a scalable solution.

The technical support from Gemini receives a rating of seven. We didn't utilize much technical support as we managed independently. They do have good technical support, though for these types of technologies, most fixes or solutions are available online.

I work with other solutions including AWS and Google GenAI services. From Microsoft Azure, we use their AI Foundry. We are using the services regarding the Large Language Models and the Document Intelligence Service from both providers. These services are quite well known within the AI community.

My relationship with Gemini is as a user. They have another partner through whom they bill, and we utilize it to develop AI applications.

Gemini is not purchased through the AWS marketplace. It is obtained directly through a Google partner. For a corporate account, they have partners through whom you can acquire it.

Based on my experience, I would rate Gemini overall eight out of ten.

Public Cloud

Amazon Web Services (AWS)

I use Google Gemini specifically to produce texts like emails, proposals, and meeting notes. My company adopted Google Workspace platform, and we have products from Vertex, so I use Google Gemini more than ChatGPT.

Google Gemini uses all the data that Google has produced. This extensive database is crucial for producing intelligence and machine learning results. Additionally, one advantage is its integration with Google Workspace, allowing easy use with Google Spreadsheets and Google Word.

I conducted some research using Google Gemini, and sometimes the results are not correct. For example, when I asked for information about marketing and inquired about the sources used, the sources were not relevant or had no relation to the subject I was researching.

I have had experience with Google Gemini for about one year.

I think ChatGPT seems to be more stable than Google Gemini.

The initial setup is easy because our base platform is Google. There is seamless integration with tools such as Google Meet and Google Calendar, which makes it very easy to use Google Gemini within Google Workspace.

I believe the price of Google Gemini is similar to that of ChatGPT, but I do not have specific cases to demonstrate this. I do know that we have subscriptions for both, but we didn't use them extensively.

I would rate Google Gemini an eight out of ten because of the dissemination of some answers. As for differences between ChatGPT and Google Gemini, one difference is that Google Gemini is integrated with Google Workspace, providing advantages for companies that use Google as their base platform. The product rating is 8 out of 10.

Google

We use AI solutions, primarily UiPath for RPA and various Generative AI tools, to develop solutions and serve our clients.

The most valuable feature of Google Gemini for us is its text writing capabilities, which we are using for writing texts and social media posts for our company's social media page. 

Additionally, we recently tested its image generation function and found it produces impressive images compared to other tools.

Google Gemini could improve its functionalities compared to other tools like ChatGPT, especially in the customization options, Canvas mode, and web search tools, which aren't as advanced.

I have been using Google Gemini for almost two years.

I would rate the stability of Google Gemini as an eight out of ten.

I would rate the scalability of Google Gemini as a seven out of ten.

I have not needed to contact customer service so far, so I would rate it as ten out of ten.

Positive

The initial setup was straightforward.

We haven't measured the return on investment so far since we use these tools to serve our clients.

The pricing is pretty average compared to other solutions.

We use various AI solutions including ChatGPT, Mistral AI, Perplexity, Claude AI from Anthropic, and tools developed in-house.

Based on my experience, I would definitely recommend Google Gemini as it is in strong competition with tools like ChatGPT and others. 

I would rate the overall solution as seven out of ten.

Hybrid Cloud

Other

My primary use case for Google Gemini is mostly for AI and large language models applied to financial services and marketing. These are the two hot areas that I am covering right now. I use federated models to serve a company by aggregating at least four to five different large language models.

Google provides an out-of-the-box solution with a comprehensive ecosystem of tools. Everything is fine-tuned, and Google offers a comprehensive package that includes compute, networking, and data storage. It allows me to manage any kind of workload out of the box. Google Gemini is very good in specific use cases.

Google can improve in model justification and interpretability of answers. I still perceive Google Gemini, in some instances, as a kind of black box. Justification of answers and explainability are two key attributes that need enhancement.

I have been using Google Gemini for the past year, particularly in its present incarnation as a recent model adopted at the enterprise level.

I rate the stability of Google Gemini as ten out of ten. Data storage capabilities and compute powers are first-class.

I rate the scalability of Google Gemini as ten out of ten. It is an excellent solution.

Google provides excellent customer service with very responsive support. A lively global community makes finding solutions easy.

Neutral

I used Zscaler primarily for LaaS, but I am not using it as much now. I am focusing sharply on AI applications and machine learning technologies.

The initial setup of Google Gemini is in line with typical, well-structured Google flows, making it comparable to other solutions.

I implement solutions for clients who have a contract with Google. I develop and extend solutions based on their contracts.

The ROI is evaluated as part of a business case before starting a project. I perform an ROI analysis to demonstrate the value generated by each dollar spent on technology.

The setup cost for Google Gemini is expensive.

I used Zscaler for LaaS.

Overall, I rate Google Gemini eight out of ten. 

Each model is optimized for delivering the best results in specific environments for specific use cases. While it is a perfect product functionally, improvements can be made in documenting reasoning flows and creating scaled-down models for specific industry verticals.

Public Cloud

Google

I have been using Google Gemini as a general conversation tool with multi-modal functions like AI OCR.

The main purpose of using the solution is to enhance internal activity and productivity. I have seen many cases of reduced costs and working hours.

I like Google Gemini because of its multi-modal function. The main purpose of using the solution is to enhance internal activity and productivity. I have seen many cases of reduced costs and working hours.

I do not have a special recommendation for improvement. It works well for my business.

I have been using Google Gemini since its release.

I rate the stability as ten out of ten.

Google's scalability is excellent due to its rich infrastructure. I have no concerns about its scalability.

I do not have an opinion about Google customer service because I have never asked for support.

Neutral

Using Google is not so easy because Google Cloud environments are a little tricky. However, there is no major issue.

The main purpose of using the solution is to enhance internal activity and productivity. I have seen many cases of reduced costs and working hours.

The pricing is very good, not expensive, and offers a reasonable price.

I have experience working with OpenAI and Google API.

I rate the overall solution as eight out of ten.

Public Cloud

Google

Right now, the main use cases for Gemini involve mostly doing experimentation on agentic AI use cases, context understanding, making sure that the context is clearly understood, and then in RAG-based applications.

The role that we use Gemini for is mostly doing the heavy lifting on different agentic AI work that we do. For example, context understanding, making sure that it generates the right result, interpreting it, summarizing it, all those things.

I have seen benefits in terms of enhancing data flow or management within my organization. In terms of Gemini, it is mostly being used to do the agentic AI work.

What makes Gemini stand out is the multimodal feature that they introduced. It was one of the first solutions where users can generate images, which is actually pretty cool.

The main benefits that Gemini brings to the table include definitely speeding things up significantly. It is also introducing many new use cases that we were not able to work on earlier. The automation part of it is fantastic.

The code generation capabilities in Gemini could be improved. When comparing it against Anthropic, they have better code generation capability.

There are specific features that should be included in Gemini in the future. Code generation exists but sometimes debugging is not as good. For Google-related technology, it works very well, but for other code, it sometimes hallucinates.

I have been working with Gemini since it was released, approximately a year ago. I have been using it extensively for different activities through APIs as well as directly through the web interface.

My advice or recommendation to other users who are looking into Gemini is that there are many options available, but from its core capabilities, especially multimodal capabilities, and from the agentic AI framework, Gemini is doing very well from that perspective. On a scale of 1-10, I rate Gemini a 9.