Generative artificial intelligence is the most impactful technological advancement transforming business operations and analytics. According to Forbes, 97% of business owners believed that Generative AI tools will positively impact their businesses. Moreover, Statista Market Insights projects a generative artificial intelligence market size to reach $207 billion in 2030.
The generative AI engineer can earn between $83,000 to $151,500 while the experts at top profiles can get about $179,000 salary per annum across the US (Source: ZipRecruiter). As of December 2023, the average salary of generative AI engineers was $115,864 annually.
If you want to delve into the ways to become a clued-up GenAI developer, then keep scrolling.
Step 1: Roles and Responsibilities of Generative AI Developers
Here are 4 general uses of GenAI -
- Chatbots
The main interaction with a chatbot is to ask if you can interact with a human. The generative AI advances, mainly vector databases and large language models, mean it holds no reality. Since chatbots are user-friendly for customers, most companies are dedicated to upgrading them.
- Natural language interfaces to software
Since modern software is feature-loaded and complicated, the user interface gets flooded with several tools, menus, and buttons. This makes it challenging for users to determine how to use it. Natural language interfaces enhance software useability by allowing the use of standard human-comprehendible sentences. These interfaces refer to either typed or spoken methods of controlling software.
- Semantic search
From finding documents, business websites, internet, to shopping websites, search is used almost everywhere. Usually, search engines use keywords, resulting in the need for search engines to be programmed to understand synonyms.
As a solution, semantic search is used to automatically search text with identical meanings. Here, the concept is simpler. You use an embedding deep learning model that changes text into a numeric vector based on the meaning. Then, it includes searching for the associated text, which is simply linear algebra.
Several embedding models facilitate different data types including audio, video, and pictures as inputs. This provides the ease of offering several input and output data types for a search. Most organizations are nowadays using semantic search to enhance the search capabilities of their business websites.
- Customized content
Another area where a generative AI developer can work is customized content creation for various groups of users. For example, modifying the product description or marketing copy based on the target audience or your understanding of the users. Localizations can also be included to enhance the relevancy of the content for various nations or demographics.
Step 2: Tools Used by Generative AI Developers
A generative artificial intelligence model is the primary need for working with text. For example, GPT 4.0. Other crucial things that you might need are a vector database like Pinecone, programming languages like Python and JavaScript, and a GenAI application framework.
Use Llama Index for developing semantic search applications and the Lang Chain framework for making generative AI applications. Remember, innovative models keep on developing constantly. Moreover, the key performers for use cases tend to change. So, use APIs because they can develop quickly. You can use open-source tools with the growth in userbase. Joining the artificial intelligence certification course is recommended to master various AI tools.
Step 3: Learn Key Skills
Python programming, mathematics, statistics, data processing and visualization, machine learning, deep learning, communication, domain knowledge and creativity, and collaboration are important generative AI skills to develop.
Start with learning one skill or tool at a time and then decide how to integrate them. While learning any tool, it’s best to set up your developer accounts. You will also need API keys for effective use of the platform.
- Vector database
An embedding model allows transforming the text into a numeric vector to show its real meaning. Then, add embedded data into the vector database. Searching simply means creating a search query and asking which database entries relate to the asked things.
- LLMs
To use LLMs, learn the ways to ask API to send a prompt and get a message. Most tasks can be performed using a single exchange back and forth with LLM. However, use cases like chatbots need a long interaction. So, master the ways to manually manage the conversation state. Also, start working with various forms of media like creating images from text.
- Integrate vector database and LLMs
RAG (retrieval augmented generation) is a technique that you must learn to process data in a way that naturally answers the queries. It means that you compose a prompt for an LLM after retrieving text from the vector database and then combine the retrieved text and prompt. Next, ask LLM to compose a human-readable answer to queries. Once you develop RAG skills, integrate them with the expertise to manage message threads for a long conversation.
Step 4: Go on learning
The final step to securing a growing AI career is to keep on learning no matter how much detailed expertise you currently have. You need to learn basic and advanced skills to get a rewarding job in the GenAI domain.
Certified Artificial Intelligence Engineer (CAIE™) by USAII® (United States Artificial Intelligence Institute) uplifts your career by helping you develop expertise and experience in the emerging AI sector. This self-paced program covers all the latest topics in just 8-10 hours/week for 4-25 weeks.
Graduate/undergraduate students and professionals with no or some experience can apply for this best AI certification program. To help you with the preparation of skill assessment, USAII® provides a verified resource center. The exam which consists of 60 questions needs to be completed within 100 minutes and requires a 70% passing percentage.
Conclusion
Generative AI has huge potential to impact people’s lives in numerous ways. Also, it has the potential to offload tasks, ensuring more productivity in personal and professional lives. The constantly rising demand for generative AI is resulting in fresh employment opportunities. If you are seeking entry or transition into this field, the abovementioned steps will help you in getting the upper hand.
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