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I decided to take a career break and try out building apps using Generative AI. I am also learning NextJS and developing LaunchStack, starter code which I will use for my web projects
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For software developers aiming to develop LLM-based applications, here are the top 5 technologies which are essential.
🔗 LangChain → https://www.langchain.com/
LangChain is a Python library that simplifies the process of building applications with LLMs by providing a modular and composable approach. It allows you to integrate LLMs with various data sources, apply transformations, and create complex workflows.
🔍 LlamaIndex → https://www.llamaindex.ai/
LlamaIndex provides tools to ingest and index data from various sources like databases, APIs, and documents. This allows LLMs to access and process specific, relevant data for generating accurate and contextual responses, overcoming the limitation of generic training data. LlamaIndex is one of the great tools for Retrieval-Augmented Generation (RAG).
🔢 Vector Databases
A vector database is used to store embeddings which are used in developing LLM applications. Examples of Vector Databases are Pinecone and Chroma.
🌐 Knowledge in Using APIs
In developing applications with large language models (LLMs), proficiency in using APIs is crucial for seamlessly integrating these models into broader systems, customizing interactions based on specific requirements, and managing complex data flows. One of the popular APIs you need to learn is OpenAIs APIs if you want to add ChatGPT knowledge in your apps. Here is the reference → https://dub.sh/JmNTWRY
Python — https://www.python.org/
Knowledge of the Python launguage is essential in developing LLM applications due to Python’s extensive support for machine learning libraries and frameworks which are commonly used for developing LLM applications.
Go — https://go.dev/
Knowledge of the Go language is essential in developing high-performance applications due to Go’s efficiency, simplicity, and powerful concurrency model, which make it ideal for building scalable and robust systems. Most AI are integrated using APIs so Go can a perfect fit. There are also Go-based libraries for AI which can be used to speed up development.
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