Only using one chatbot? It's better to use multiple
Juggling chatbots may seem chaotic, but it’s the smarter way to master AI without letting one tool box you in.
You’ve heard the advice: simplify your tools and stick to one system. But the truth is in the realm of AI chat, sticking to just one tool is limiting.
Using multiple chatbots might sound like overkill, but with the right strategy, it’s a smarter way to work. Authors crafting stories, engineers solving technical challenges, or even artists refining concepts can benefit by expanding AI tools.
Key Takeaways:
Diversifying AI chat tools enhances productivity by leveraging their strengths
Selecting 2-3 complementary chatbots prevents overwhelm while maximizing efficiency
Evaluating your specific needs will identify the best chatbot to add to your mix
The case for AI variety
Why isn’t one chatbot enough? Because no single tool excels at everything.
The core idea is this: every AI chat tool lacks something that others offer. The most obvious example being that ChatGPT can’t access the internet. Using two chat tools allows us to be more effective by combining tools that complement each other without redundancy.
5 main points of diversification
Chat tools differ in five core areas of functionality. They are:
Ephemeral threads vs. long-standing conversations. Not all tools allow the same level of continuity.
Internet access vs. static LLMs. The difference between real-time access to the internet (Copilot) versus a more advanced LLM with a static knowledge base (ChatGPT, Claude).
Separation of core prompting functions. Each tool has strengths in different areas of creativity and logic. Some are better at math, some at coding, others at reasoning, but you have to look very closely in to see it.
Managing memory limitations. Even the best tools have limits, which can be thread-wide (Claude limits thread length on their free plan).
Handling specialized domains. Your broader org or tech stack can dictate. CoPilot for Microsoft, Gemini for Google.
But even outside of missing functionality, having a second chatbot is a key defense against biases and hallucinations. AI models have their moments, so using more than one can expose blind spots. And you don’t have to go further than copying the same prompt into two tools to find them.
Criteria when choosing your chatbot squad
Not every chatbot deserves a spot on your team. Here’s how to find the right mix for your workflow:
Evaluate data privacy preferences: Most tools allow you to opt out of training data, but it’s good to be sure. Decide if you’re comfortable with being a part of training data.
Assess your experience with AI tools: If you’re new to AI, an intuitive tool like ChatGPT can help build confidence. Advanced users might prefer Claude AI or Groq.
The recency of data: If you need current, real-time data, prioritize tools with internet connectivity, like Microsoft CoPilot. For content or less “bleeding edge” tasks, static tools like ChatGPT work just fine.
Your problem-solving needs: Different tools shine in different areas. Need strong math and logic capabilities? Go with Groq. Need conversational creativity? Try Google Gemini.
Check for mobile access: Flexibility matters. Some tools, like Claude and ChatGPT, have particularly strong mobile apps.
Outside of pairing functionalities, having a second or third chatbot can be a key defense against biases and hallucinations.
A self-questionnaire
So if you’re asking yourself what another good chatbot might be for, here’s three questions to ask yourself:
What tasks do you currently use a chatbot for? (Consider the different prompt purposes)
Generation: Writing content, brainstorming, or generating code
Assimilation: Analyzing data or converting it
Research: Fact-checking or exploring trends
Summarization: Condensing articles or summarizing long-form content
Which tasks are either missing or not easy to do with your current tool?
Generation: Does it lack creativity or provide bland writing? Is there no style variety? For engineers, is the code wildly off?
Assimilation: Does it struggle with data you give it, in particular numerical? Does it handle basic tables when you paste them in?
Research: Can it access the internet? Is any data or conceptual knowledge out of date for your particular domain?
Summarization: Too verbose or inconsistent?
Which chatbot is the best addition to your stack? (Matching a tool to your gaps)
Generation: Use Google Gemini or ChatGPT for creative tasks or ChatGPT for versatile tone adaptation. If you’re generating code, it’s Claude AI.
Assimilation: Try Claude AI for long memory. Both Claude AI and Groq excel at technical precision.
Research: Use Microsoft CoPilot for real-time internet access, particularly great for tech or medical related research.
Summarization: Both Claude AI and ChatGPT bring a superior mix of depth or conciseness.
In other words, it’s a three-step process where you map your current use, identify your needs and make an intelligent addition to the AI stack of your dreams.
How many is too many?
Adding too many tools can backfire, sowing confusion instead of productivity. The best guidelines for keeping it manageable are:
Practical limit = 2–3 tools: For 90% of folks, 2 well-selected chatbots can cover almost every need.
Balance cost and value: Using extra tools doesn’t mean extra subscriptions given the prevalence of free plans.
Avoiding redundancy: Assign each tool a clear role in your workflow. Like ChatGPT and its long threads for a project-specific chat.
Using multiple chatbots doesn’t create chaos—it can fill gaps in your workflow. The right mix of tools means we can tackle tasks with a greater level of precision. And more importantly, speed. And more important yet, accuracy.
Links
Here are links to all of the tools mentioned in this post: