Claude

What I appreciate most about Claude lies in its efficiency, reliability, and specific areas of excellence. One of the primary use cases where Claude excels is creating Proof of Concepts (POCs) for data visualization. When clients present a challenge, I first use Claude to summarize and rephrase the problem into a specific and technical definition. Then, I leverage Claude to create a dashboard or visualization POC using synthetic data based on that description. This process allows me to generate a POC ready for client presentation within approximately 20 to 30 minutes, even with iterations. I successfully used this approach with a manufacturing client who wanted a sophisticated financial dashboard with specific KPIs beyond their existing Excel reports. Claude helped by taking the summary of our meeting, defining the client's pain points, and then generating a technical definition of the required dashboard and potential KPIs. Subsequently, Claude developed a POC with different graphs and KPI definitions, which proved very successful in our next client meeting and ultimately helped us close the deal. Coding is another area where Claude truly shines. I use it as a coding assistant for various programming languages. When testing the results, Claude often provides a good version more easily than other platforms used for coding assistance. In fact, I believe many current AI coding tools might use Claude under the hood because it is, in my opinion, the best for coding and solving math problems compared to other LLM models. Claude also excels at creating content for writing purposes, such as drafting a blog post, especially when provided with specific context and a detailed description of the requirements. I believe it surpasses other tools and other models in this regard. In terms of accuracy and reliability, I rate Claude highly. Despite the potential for hallucinations, if faced with a choice among various AI tools, I would trust Claude the most for my purposes, especially for building POCs. I specifically prefer Claude over other AI tools for coding and generating marketing content. A notable strength is Claude's capability to handle long-form context retention. It can manage multi-nested conversations and work with large documents effectively. I've also found the customization options to be excellent, based on my extensive reading and learning. The key factors that drove my decision to use Claude were its product functionality and performance, alongside a strong user community. The reasons for adopting it included improving business process agility and outcomes, reducing time to market, and driving innovation.

Very powerful coding models, pretty much the standard.

The reasoning capabilities when loading code repos and input for analysis, I am able to very efficiently drill-down on issues and find measures. This helps me communicate with lead developers.

While my overall experience using Claude has been very positive, there are a few specific areas that I believe present opportunities for improvement, or that I would identify as dislikes. These aspects of product capabilities, indicating that while it excels, there is still room for further development and feature enhancement. One of the most significant limitations I have consistently encountered pertains to Claude's research capabilities, particularly its historical inability to navigate the internet. For any task requiring extensive research, Claude has, until very recently, proven less effective because its responses rely solely on its pre-trained data. This meant that for me, using Claude for in-depth research was not viable as it lacked the crucial ability to conduct real-time internet searches for current or specific external information. This inherent constraint compelled me to utilize other platforms, specifically for research purposes. It effectively meant that Claude could not serve as a comprehensive, single-source solution for tasks that necessitated both content generation and real-time information gathering. I understand that this deep research option, or the ability to navigate the internet, may have been launched very recentlyI recall reading some news about it just yesterdaybut I have not yet had the chance to try it out. Consequently, for the duration of my primary usage, this has remained a notable weakness, forcing me to segment my workflow and employ external tools whenever up-to-date or external data validation was required. Despite this, it is interesting to note that even with this known limitation, and the fact that other tools might access more updated data, my trust often defaults to Claude over others. However, for pure research, this has been a clear area where Claude lagged behind. Another aspect that I find somewhat limiting is related to context retention, especially during very long or multi-nested conversations with Claude. While Claude is certainly capable of handling multi-nested conversations and working with large documents, a consistent pattern I have observed is that when engaging it extensively in this manner, I frequently encounter a message indicating that I am approaching or have reached a quota. This means that if I continue to have longer conversations, I risk hitting a usage limit. This specific quota issue is something that, in my experience, does not typically manifest with other comparable tools. It feels like a significant practical weakness in the user experience, as it can interrupt a detailed and ongoing line of inquiry. While I can only speculate, it is possible that this quota system is in place as a measure for Entropic to manage and keep their operational costs low. Regardless of the underlying reason, from a user perspective, this interruption can be disruptive, particularly when I am deeply engaged in a complex project that requires continuous, extensive conversational interaction and context memory from the AI. Furthermore, from a broader, more strategic perspective regarding the evolution of generative AI tools, I would like to see Entropic, the company behind Claude, work towards integrating or collaborating with other AI models to enable the generation of different types of content. Currently, Claude excels remarkably at what it does within its domain, primarily focused on text-based outputs like coding, writing, and problem-solving. However, I believe there is an opportunity for it to become a more all-around content generation tool. This would entail expanding its capabilities to include the creation of images, video, or audio content. While Claude is undoubtedly best in class for its current functions, an integration with other generative AI models would allow it to offer a more comprehensive suite of creative services, eliminating the need for users like myself to switch between multiple specialized AI platforms for different media formats. This expansion would position Claude as a truly holistic solution for various content creation needs. In terms of minor operational inconveniences, I have occasionally experienced a slight technical quirk. Though it is quite rare, sometimes after I send a prompt, Claude appears to enter a state of indefinite thinking, where no response is generated. The interface remains active, but the output simply doesn't appear. However, I have found a workaround: if I refresh the page, the answer I was waiting for typically appears immediately. This is a strange phenomenon, and while it doesn't happen often, it is the only instance of an accessibility or response speed issue that I have noticed during my time using the platform. It is a minor disruption, but it does break the seamless interaction momentarily. Finally, a point that contributes to my inability to rate Claude a perfect for accuracy and reliability is the inherent characteristic of Large Language Models: the risk of hallucinations. Claudes accuracy and reliability is not a perfect score precisely because this risk always exists. Compared to other platforms and LLM models, I do find Claude to perform very well, and if I had to choose between various options and Claude, I would trust Claude the most. However, this underlying risk means that we never use Claude for actual final deliverables. My organization and I strictly utilize it for building POCs and MVPs. Once a client approves a POC, we then proceed to build everything from scratch, ensuring that the final product is entirely free of any potential inaccuracies generated by the AI. This cautious approach, necessitated by the possibility of hallucinations, is a key reason why it does not receive a perfect score for reliability in my assessment, even though it remains my preferred tool.

Too expensive! The per token cost is pretty crazy.

Overly long responses repeating the same outcome in slightly different wording, I would prefer tighter answers faster and drill-down if I need to. Too high confidence level on areas with low documentation or context, I would prefer a more transparent view on results, and not statements like this is a very common problem in topic X when the domain problem I am working on has very little recent documentation. Lack of multi-modal capabilities is sometimes an issue when my sources have a lot of technical documentation. Scaffolding new projects with old libs sometimes requires clean-up activities.