Questions to Ask New Hires After 30 Days

Checking in with new hires after 30 days ensures they are adjusting to their roles and have the support they need. These check-ins help address concerns and enhance future growth. Here are relevant questions to ask new hires during this period:

1. How are you finding your onboarding experience so far?

Starting a new job can be overwhelming. Asking about their onboarding experience shows your concern for their comfort and readiness to address any challenges.

2. Are there any tools or resources that would have been helpful during your onboarding?

It's essential for new hires to have access to necessary tools and resources. This question helps identify gaps in the onboarding process for continuous improvement.

3. How do you feel about the training you received for your role?

Training is vital for new hires to understand their responsibilities. Asking for feedback on training received helps assess if adjustments or additional training are necessary.

4. Are you comfortable asking questions and seeking guidance when needed?

Creating an open and supportive environment is important for new hires. This question allows them to share any concerns about seeking help.

5. What has been your most significant challenge during the first month?

This question encourages new hires to reflect on their experiences and share challenges. It provides insights into areas requiring additional support or improvement in onboarding.

6. What aspects of your role are you enjoying the most?

Understanding what new hires find enjoyable can help identify their strengths. It shows your interest in their experience and promotes a positive mindset moving forward.

7. Are there any areas where you feel you could use additional support or training?

Encouraging new hires to reflect on their development needs sets the stage for growth. Identifying areas for support or training helps in providing necessary resources.

8. How would you describe the work environment and your colleagues?

Feedback on the work environment and colleagues is crucial for employee satisfaction. This question helps identify any potential issues and fosters a positive workplace.

9. Do you feel that your skills and expertise are being fully utilized in your current role?

Understanding whether new hires feel their skills are effectively utilized is essential for satisfaction. This question can reveal opportunities for skill development or role adjustments.

10. How do you see yourself growing within the company in the future?

Discussing career aspirations shows your commitment to their growth. It helps align their ambitions with the company's objectives and fosters long-term engagement.

These questions initiate important conversations and feedback. Listening to new hires and addressing their concerns illustrates your commitment to their success and team building.

Create your AI Agent

Automate customer interactions in just minutes with your own AI Agent.

Get started for free Chat with AI for fun

Featured posts

The Growth of RAG in AI

RAG, or Retrieval Augmented Generation, is a way to make AI smarter. Here’s how it works: Retrieval means finding information, Augmented means enhancing something, and Generation is about creating new content. So, RAG improves AI by helping it search for relevant information before providing an answer. This means the AI doesn’t just make guesses; it looks up information to ensure its responses are accurate.

Anthropic's Model Context Protocol: Connecting AI to Any Data Source

In the ever-evolving world of artificial intelligence, one of the most significant hurdles has been the isolation of AI models from the vast array of data sources that could enhance their capabilities. Anthropic, a leader in AI innovation, has just announced a groundbreaking solution to this problem: the Model Context Protocol (MCP). This open-source protocol promises to transform how AI systems interact with data, making them more connected, efficient, and relevant.

What is an LLM and What Can It Do?

A large language model (LLM) is a type of artificial intelligence that processes and generates human-like text. These models are trained on vast amounts of text data and are designed to predict and create coherent sentences. LLMs, like ChatGPT, can understand context, provide detailed answers, and carry conversations almost as fluently as a human. Their capabilities make them valuable tools across industries, enhancing productivity and improving services.

What is WebRTC and Why is it So Useful??

WebRTC (Web Real-Time Communication) is an open-source technology that allows web applications and websites to capture, share, and exchange multimedia (video, audio, and data) directly between browsers, without the need for third-party plugins or software. In simple terms, WebRTC enables real-time communication directly in your browser, making it easier for developers to create video chat applications, file-sharing tools, and other interactive communication services.

Halloween Traditions: What to Wear and What to Eat

Halloween is a time when creativity comes alive, blending spooky fun with the warmth of autumn. As October 31st approaches, people begin planning their costumes and preparing festive treats that capture the spirit of the season. Whether you’re dressing up to scare or celebrate, or looking to make some themed snacks, here’s a guide to Halloween traditions, covering what to wear and what to eat.

Fine-Tuning Large Language Models: A Comprehensive Guide

Data labeling is a foundational process in the development of AI systems. It involves annotating raw data to make it understandable for AI algorithms. Whether it’s training a chatbot, enabling self-driving cars, or improving healthcare diagnostics, data labeling is a critical step that ensures AI systems can learn, reason, and make decisions effectively. This article explores what data labeling is, its importance in AI, and how it shapes the future of intelligent systems.

What is a Large Language Model?

Large Language Models are a fascinating aspect of AI. They are powerful systems capable of processing, analyzing, and generating human-like text. These models can perform various tasks, making them a versatile tool in modern technology. In this article, we'll explore what a large language model is, whether it is considered AI, what it consists of, what it can do, and how it is made.

Getting Started with Google Vertex AI

Google Vertex AI is a comprehensive platform designed to help developers, data scientists, and businesses build, deploy, and manage machine learning and artificial intelligence models with ease. Here’s a detailed guide on what Vertex AI is and how you can start using it.

Achieve more with AI

Enhance your customer experience with an AI Agent today. Easy to set up, it seamlessly integrates into your everyday processes, delivering immediate results.

Try for free Get a demo

Latest posts

AskHandle Blog

Ideas, tips, guides, interviews, industry best practices, and news.

• February 7, 2025

vLLM: Supercharging Large Language Model Inference

Large language models (LLMs) are transforming industries, but deploying them efficiently can be a challenge. vLLM.ai offers a solution: a high-throughput and memory-efficient inference and serving engine designed specifically for LLMs. It allows developers and organizations to serve these powerful models with significantly improved speed and reduced costs. This article will explore what vLLM is, how it works, and the benefits it provides.

vLLMLLMAI

• November 5, 2024

Why Is Your Company Struggling to Scale Up Generative AI?

Many companies are eager to adopt generative AI to enhance their products and services. Yet, moving from initial implementation to scaling AI effectively can be challenging. If your business is facing these obstacles, you’re not alone. Let’s examine some common hurdles that may be holding back the successful integration and expansion of generative AI.

EmployeesGenerative AIAI

• August 14, 2024

What Is the Brief Process of Training a Large Language Model?

Training a large language model like ChatGPT or LLaMA is a complex and resource-intensive process that involves several stages. These models, which are based on transformer architectures, require vast amounts of data, computational power, and time to achieve the high level of performance expected from them. Below is a simplified overview of the key steps involved in training such a model and an estimate of the time required.

LLMTrainingAI

View all posts