Tools Every GenAI Engineer Uses Daily

GenAI is evolving quickly. Just a few years ago, working in AI meant spending weeks cleaning data to train a model that might only reach 80% accuracy. Now, you can call an API and get human-level reasoning in seconds. If you want to become a GenAI Engineer, you’ll need tools for memory, logic, testing, and deployment. In this article, I’ll show you the tools GenAI Engineers use every day.

Tools Every GenAI Engineer Uses

These are the tools GenAI Engineers use daily and why you should learn them.

The Orchestrators: LangChain & LangGraph

Picture having a brilliant employee, the LLM, but they have no memory, can’t browse the web, and don’t know your company’s internal documents.

LangChain solves this problem. It connects the LLM to tools like Google Search, Wikipedia, and your database.

The industry has moved from simple chains, where you go from step A to step B, to agents that let the AI decide what to do next. LangGraph helps with this. It’s now the standard for building complex agents that avoid getting stuck in endless loops.

Here are some practical resources to master these tools:

The Memory Banks: Vector Databases

LLMs can only read a limited amount of text at once, called the context window. You can’t paste a whole 500-page textbook into the prompt.

Learn Vector Databases (like Pinecone, ChromaDB, or Weaviate).

These databases store data as vectors, which are lists of numbers that capture the meaning of the text. When someone asks a question, the system finds the most similar content in the database and sends only that information to the LLM. This method is called RAG, or Retrieval-Augmented Generation.

Start with ChromaDB since it runs locally on your laptop and is free and easy to use. Switch to Pinecone when you’re ready to deploy to the cloud.

Here are some practical resources to master vector databases:

The Model Hub: Hugging Face & Ollama

You might not want to pay OpenAI for every query. Sometimes you need privacy or want to run a model on your own laptop without needing the internet.

Hugging Face is like the GitHub of AI. The open-source community shares models here. You can download models for medical data, coding, or creative writing.

Ollama is also a great tool for students. It lets you run powerful models like Llama 3 or Mistral on your MacBook or PC with a single command: ollama run llama3.

Running models on your own computer helps you learn about latency and computing.

LlamaIndex

I mentioned LangChain earlier. Still, LlamaIndex deserves its own mention.

LangChain is great for building agents that take actions, while LlamaIndex is better for handling data and reading information. If you need to process thousands of messy PDF reports and make them searchable, LlamaIndex is often faster and more effective at organizing the data than LangChain.

Here’s the rule of thumb:

Building a Chatbot that takes actions (books flights, sends emails)? Use LangChain.
Building a Search Engine for messy documents? Use LlamaIndex.

Closing Thoughts

Here is my final piece of advice: the tools will change.

Two years ago, LangGraph didn’t exist. Three years ago, Pinecone wasn’t around. So, instead of just learning LangChain, focus on understanding orchestration. Instead of only learning Pinecone, learn about vector similarity.

If you found this article useful, you can follow me on Instagram for daily AI tips and practical resources. You might also like my latest book, Hands-On GenAI, LLMs & AI Agents. It’s a step-by-step guide to help you get ready for jobs in today’s AI field.

Tools Every GenAI Engineer Uses Daily