As advance forward understanding the use of Large Language Models (LLMs) in your business, numerous effective usage can be found. At this stage where the use is still highly debatable people are still looking for effective ways to build your AI ready workforce. An approach we are going to take into consideration through this blog is use of Multiple LLMs for the benefit of your business.
Yes, you may have already learned the initial benefits of Gen AI agents and bots. However exploring one particular, i.e; ‘Enhances Team Collaboration’ many companies believe there resides true efficiency. Post-exploration stage companies have discovered that using AI agents with multiple LLMs is impressive, How? That is what we are going to understand in this article uncovering AI for workplace productivity.
What is a Multi LLM Platform? Made for AI Ready Workforce
Definition: A platform where the user has access to multiple large language models. It helps the user to perform various tasks that require different Multiple LLMs using a single dashboard.
OpenAI was the first company to introduce ChatGPT, a GenAI chatbot helping people to perform complex tasks in no time. People later discovered that ChatGPT is a large language model itself, well to be specific families of LLMs. Past 3 years now we are at a stage where multiple tech giants have already launched their own large language models.
Popular Large Language Models
Above mentioned are some of the most popular LLMs. Each has its own key features, achieved parameters, use cases, and provides access to different sets of information.
So it really comes down to the diversity of solutions that we as a user are being presented with. First you must have believed that AI as a single solution to your everyday problems. But now after being presented with numerous options, multiple LLMs, it surely has become complex. As for work it raises a question which AI in the workforce will provide significant value.
How about finding every multiple LLM at one place? This way you can use one LLM to decide the structure of a report and another to write the report itself. Don’t worry you are not the only one using multiple LLMs, many people do and hence the time for Multi-LLM platform had to be originated.
Now that you have a hint of what a multiple large language model is, let’s focus on the “Why?” part. However, with the introduction of Multi-LLM AI as a service product, people do raise questions. “Why would I use multiple models; LLMs with different features?”. Let’s break it down shall we?
Importance of Multi-LLM Strategy for AI Ready Workforce
Every business needs to spin ideas faster each quarter. Whether it’s strategizing, performing, or acting with resilience, you and your team need to outsmart competitors. Companies are already geared up for integrating Gen AI in the workforce & businesses.
Image Source: Microsoft Work Trends & Index
A multi-LLM strategy is all about eliminating friction that you might have faced while using separate platforms. Prominently the strategy favors teams which have a diverse ratio of skilled employees.
Let’s take an example of digital agencies to understand multiple model LLMs.
- Primary challenges that agencies generally face:
- Content Strategy against Data Overload – seeksocial
- Increasing Competition – droptica
- Slow Sales Cycle – DMN
- LLM Required: Claude, GPT-4, Midjourney, Bard.
- Claude for research, document analysis, and helping with trivial tasks.
- Bard for increasing creative output and researching a strategy.
- ChatGPT-4 for content creation, daily tasks, and innovating a new strategy.
- Midjourney for visual content creation.
- Meta’s Llama for custom solutions as it is self hosting open source.
- The biggest bottle neck:
Each team member requires different LLMs adhering to their task requirements. Sure a team can use multiple LLM models but ineffective monitoring results in distrust among team members. Lastly, the varying cost and complexity of integration leaves your company to question the skill of your AI ready workforce itself.
- Solution: A platform with multiple LLMs, easy to navigate user interface, and a monitoring dashboard on administrator side.
- Improved SEO rankings, faster content production & effective content management.
- Reduced in initial ideation, increase in iterations, and enhanced collaboration.
- Better project timeline accuracy, dodging project delays, and detailed report generation.
Best Practices for AI Ready Workforce Using Multi LLMs
Below are 5 key strategies helping you and your team to understand how to integrate multiple LLMs for your business.
- Selectivity: Use general LLMs like ChatGPT-3 and Gemini for everyday tasks, on the other hand use high end LLMs like ChatGPT-4o for complex tasks. An AI ready workforce is all about matching your team’s strength to the model’s capabilities you are going to use.
- Implement Strategic Routing: Using multiple models is about unlocking efficiency. For example, have a tool that combines all the necessary LLMs you need. This will save cost and streamline your workflow for better performance.
- Chain Models: For general analysis use go-to models. Once you strike the perfect chord, use that general analysis and feed it to models that can rewire your ideas and match the complexity you are diving deep into. For example: initial classification of content and generating ideas can be done using Gemini and for creating content strategies or content campaigns with the team use Claude and ChatGPT-4o.
- Maintain Consistency: Whether it’s in the output formats or providing a prompt, always maintain standardized format for every task. For example, have a custom bot, prompt library, prompt templates or refer to our effective prompting strategies for better results.
- Monitor: A necessary operation about having AI in the workforce is analyzing reports by monitoring performance of each model. It gives you an idea of which large language model is working for your team in the best way. Always consider cost, latency, task accuracy as your primary KPIs by performing a SWOT analysis on each LLM.
Build Your AI Ready Workforce with Us
New age AI users often are quick to notice even the slightest deviation in an LLMs output. Yes your team is becoming smart and are ready to adapt a new workflow where AI is helping them to generate optimal output. Users who have enthusiastically adapted AI noticed how they are able to save time, be more creative, and love what they are doing.
Looking at the initial stage of integrating AI in the workforce into your business, those are the core strengths which have surfaced. In an age where quantifying productivity is everything. Rather than an uncertain future, it’s better to have a strategy, a multi-LLM strategy for you & your team to keep up with the competitors.