Different Large Language Models (LLMs) for different results

Kyloe AI Assist gives you the flexibility to choose the Large Language Model (LLM) that best suits your needs.

Why is this important?

• Specialisation: Different LLMs can be fine-tuned for specific tasks or industries so, for example, could improve the accuracy more of candidate matching.

• Bias Reduction: By cross-referencing outputs from different models, recruiters can achieve a more balanced and fair assessment of candidates.

• Enhanced Decision-Making: Different LLMs can provide varied perspectives and insights, enriching the decision-making process. This diversity in AI opinions could lead to more comprehensive evaluation of candidates.

By leveraging the strengths of different LLMs, Kyloe gives you more control and autonomy in your AI journey making sure your candidates and customers get the very best experience working with you.

Here’s a quick and easy breakdown to help you decide:

When to use Claude

• Claude 3.5 Sonnet: Choose this for a balance of speed, intelligence and cost-effectiveness. It's great for general-purpose tasks, creative writing, and complex reasoning where you need high-quality output.

• Claude 3 Haiku: This is your go-to option when speed and responsiveness are paramount. It's the fastest model in the Claude 3 family. Perfect for real-time interactions, quick summaries, and tasks where you need immediate results without sacrificing too much accuracy.

When to use GPT

• GPT-4 Turbo: Select this for tasks demanding the most advanced reasoning, complex problem-solving, and nuanced understanding. If accuracy and the ability to handle intricate instructions are critical, GPT-4 Turbo is an excellent choice.

• GPT-4o: This model shines when you need a versatile model that handles text, vision and audio well. It's particularly suited for multimodal applications, creative projects, and interactive experiences that benefit from a blend of different types of inputs.

• GPT-4o-mini: A smaller and faster version of GPT-4o. Best used where you need a super-fast model to produce high volumes of content, with cost being a significant consideration.