Why Diversity in AI Development Teams Is Key to Better AI Solutions The future of AI depends on diversity. Learn how diverse teams improve creativity, ethics, and problem-solving. #aidevelopment #llmdevelopment #diversity #workplace #hr #techhr buff.ly/4iXSO4m

Quick Answers, Smarter AI EfficientRAG is your solution for delivering fast, accurate answers to complex queries. Find out how it works. #aidevelopment #llmdevelopment #llmevaluation buff.ly/4gExP4P

HubSpot’s journey to success has been legendary. Hear it straight from Co-Founder Brian Halligan on leadership, strategy, and growth: buff.ly/3VTBd3h

Deploying RAGs in Production: A Comprehensive Guide to Best Practices Production RAG systems need careful planning. This guide covers everything you need for successful deployment. #aidevelopment #llmdevelopment #llmevaluation buff.ly/40bq5Bo

What Are the Key Metrics for LLM Evaluation? Evaluating LLMs? Discover the essential metrics to measure your models and drive better performance. #aidevelopment #llmdevelopment #llmevaluation buff.ly/4fCPxnO

How do you prioritize model accuracy vs. inference speed in your deployments?

Dos and Don’ts During RAG Production Avoid the common pitfalls in RAG production. Follow these dos and don’ts to build successful systems. #aidevelopment #llmdevelopment #llmevaluation buff.ly/40cQMFY

Navigating AI product management in highly regulated sectors? This playbook has everything you need to succeed: buff.ly/4gBvc3K

10 Essential Tools Every AI Developer Should Know in 2025 2025 is around the corner. Discover 10 must-have tools that will transform how AI developers work. #aidevelopment #llmdevelopment #llmevaluation buff.ly/4gwYRLf

What’s your go-to tool for debugging AI workflows?

What’s the most overlooked factor when fine-tuning LLMs?

Do you think symbolic AI will make a comeback alongside LLMs? Why or why not?

How do you approach dataset augmentation for niche AI tasks?

What’s one feature missing in current AI IDEs that would save you hours each week?

How do you determine when to switch from open-source models to proprietary solutions?

What’s your preferred method for evaluating the performance of multi-agent systems?

Which is harder: training a model or explaining its results to stakeholders?

How do you balance data security with the need for diverse training datasets?

Do you see RLHF as the future of improving AI behavior, or is there a better alternative?

Build and Test AI pipelines using @openai, Firecrawl, and Athina AI While building a production-grade LLM application, it is critical to test your AI pipeline on a dataset specific to your use case/domain.