30 May 2025, 1:41 pm
From GPT-4 to LangChain & AutoGen - Here’s Your Ultimate Tech Stack for Generative AI
The world of AI is evolving at lightning speed. Simply relying on “Just ChatGPT” isn’t going to cut it anymore. Today's AI professionals need to be adept at building, connecting and fine tuning intricate systems that utilize a variety of frameworks and models.
At the School of Core AI, we empower working professionals to move beyond basic prompts and explore real world applications with a proven tech stack. In this blog we will introduce you to 8 essential tools that are paving the way for the future of AI workflows.
1. GPT-4 (OpenAI)
GPT-4 is a cutting edge Large Language Model (LLM) from OpenAI designed to generate, summarize, reason, code and respond with an impressive level of human like fluency.
Use Cases: Chatbots, code generation, document automation, reasoning agents and customer support AI.
Who should learn this: Software engineers, data scientists, technical product managers.
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2. LangChain
A Python and JavaScript framework that allows you to build applications with LLMs by linking together models, tools, memory and agents.
Use Cases: Retrieval Augmented Generation (RAG), AI chatbots with memory, multi-step workflows and API integration with LLMs.
Who should learn this: Developers creating custom AI applications, backend engineers and prompt engineers.
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3. AutoGen (by Microsoft)
A multi agent framework designed for creating collaborative AI agents that can tackle structured tasks, share memory and work together seamlessly.
Use Cases: Autonomous research agents, AI assistants, distributed workflows and data labeling agents.
Who should learn this: AI engineers, MLOps professionals and automation developers.
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4. DeepSpeed
A powerful deep learning optimization library designed to help you train large models more quickly and affordably brought to you by Microsoft.
Use Cases: Speeding up LLM training, inference on limited hardware (think 1–2 GPUs) and fine tuning those massive models.
Who should learn this: ML engineers, AI infrastructure architects and researchers who are working on custom LLMs.
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5. Hugging Face Transformers
The go to open source hub for pre trained NLP and vision models like BERT, T5, GPT2, CLIP and many more.
Use Cases: Text classification, embeddings, translation, QA systems, sentiment analysis and named entity recognition (NER).
Who should learn this: NLP engineers, AI enthusiasts, researchers and students eager to explore real world datasets.
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6. Runway ML
An easy to use interface that allows creatives and developers to harness AI for images, videos, audio utilizing models like Gen-2 and Stable Diffusion.
Use Cases: Text to video, image enhancement, generative video editing and AI content creation.
Who should learn this: Creators, media professionals, marketers, YouTubers and UI/UX designers.
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7. Gradio
A handy tool that lets you create user friendly interfaces for your machine learning models with just a few lines of Python code.
Use Cases: AI demos, model validation interfaces, shareable prototypes and LLM sandboxing.
Who should learn this: Data scientists, ML engineers, hackathon participants and product teams.
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8.LLM Guard
Think of it as a safety net for LLM responses it identifies prompt injections, protects against PII exposure and filters out toxic outputs.
Use Cases: It’s essential for securing AI agents, moderating content and ensuring privacy compliant LLM systems.
Who should dive into this: AI safety experts, security teams and enterprise AI developers.
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These tools aren’t just for researchers or startups.
They represent the essential skill set for:-
Whether you’re a backend developer, a data scientist or just starting your journey in AI mastering this toolkit will keep you ahead of the race.
At the School of Core AI, our courses are designed to teach systems level thinking not just how each tool works in isolation but also how they work together to build intelligent apps.
Explore our top programs:
AI isn’t a mystery anymore. The future is for those who can weave together models, tools, frameworks and safety layers into a cohesive and intelligent system.
Don’t just learn to prompt.
Learn to create.
Visit the School of Core AI to discover hands-on AI programs tailored for professionals.
