The rise of LLMs isn’t just hype-it’s changing the software development landscape. From chatbots to document Q&A, applied AI is now part of the core tech stack.
So, how can developers catch up and start building with it today?
💡 Here’s a practical approach:
Use LangChain to Orchestrate AI Workflows
LangChain makes it easy to chain LLM calls, tools, and memory into powerful applications—without reinventing the wheel. Whether it's document search, conversational agents, or code generation, it’s a game changer for rapid prototyping.
Leverage AstraDB for Scalable Vector Search
Combine LangChain with AstraDB to store and query your embeddings using built-in vector search capabilities. It's serverless, fast, and integrates smoothly with the LangChain ecosystem—ideal for deploying production-grade AI features.
Focus on Solving Real Problems
Don’t get lost in the math. Applied AI is about delivering value. Whether you're helping users search smarter, automate responses, or summarize content—start with the problem, not the model.
Build and Share
Use open-source tools and cloud services to build your own LLM-powered side project. Share your learnings. Contribute. The best way to master AI is to use it.
🔍 Being AI-savvy doesn’t mean becoming a researcher—it means being a builder who knows how to apply powerful tools like LangChain and AstraDB to real-world problems.
Curious how others are integrating LLMs into their workflow? Let’s talk 👇
Top comments (2)
Excellent breakdown — love the emphasis on solving real problems over chasing complexity. LangChain + AstraDB is a powerful combo for bringing applied AI into production. Looking forward to seeing more practical use cases from the community!
Thanks, @williamoliver!
I’ll share more practical use cases with links to deployed apps once they’re ready. I’m currently working on several AI and LLM-powered projects, most of them are still private for now, but I hope to have something to share soon!