Abstract
Generative AI and Prompt Engineering has
brought a new era in machine capabilities, where systems capture the natural
language and create a customized new output. Taking references from various
sources, the Gen AI compiles a unique focused output as required by the user
via the series of question and answers, where were strategically laid out using
Prompt Engineering. Using historical data and responses, the questions are
formulated and positioned carefully to guide the user to provide responses that
best explain the needs. This information collected from various sources are
properly structured and conditional sequence defined that can dictate the
solution path to take and there by identifying the right answer. This can be leveraged
in any part of the business operations to guide the users and automate the
solution provision. With Oracle ERP and Cloud being one of the most structured,
with every validation and flow clearly defined, using Gen AI and Prompt
engineering, the organizations can build a comprehensive solution that can work
with the users in educating and guiding them on the process and by providing
solutions to the problems, which can effectively eliminate the support needed
from the experts or support team, thereby eliminating the dependency and
reducing the turnaround time.
Keywords: Gen AI, Prompt engineering, Generative AI, Machine learning,
L1 automation, Natural language processing (NLS), Interactive learning,
Adaptive programming, Operational assistance using AI.
1. Introduction
“Generative AI or generative artificial
intelligence refers to the use of AI to create new content, like text, images,
music, audio, and videos.”
-Definition
by Google Cloud
(1)
“Prompt engineering is the process where you
guide generative artificial intelligence (generative AI) solutions to generate
desired outputs.”
-Definition
from AWS (2)
Generative AI and Prompt Engineering are two closely
related technologies that so each other in providing services to the end user.
With carefully planned and positioned questions prompt engineering gathers all
required information that is fed into the generative AI for creating customized
solution. Oracle has fused these capabilities into their cloud systems to
provide easy come off fast and seamless assistance to their end users in
various departments and business divisions enabling them to complete their task
easily with minimal he had us and friction.
2. Oracle Generative AI
Oracle has plugged in the power of Generative
AI in every part of its cloud solution, from its Infra (IaaS) to its
integration solution (PaaS) to the cloud ERP (SaaS). These Generative AI use
the Large Language Models (LLM) to understand the user request in its natural
form and translate the into machine language,
which in-turn would identify the specific performers, known as Agents, to
accomplish the task. These agents designed and structures similar to a
functioning business, such that they are self-sufficient to plan, manage and
execute the tasks.
2.1. Generative AI agents
This is a solution, where generative AI is
provided to assist the end users in various functional activities. These range
from providing answers or information requested, to extremely sophisticated
tasks, like working in tandem with the users and continuously monitoring them
and guiding them on business practices. For example, using these generative API
capabilities users can inquire about an outstanding payment or ask about a
certain transaction that failed to understand the underlying problem. It can
also be used to automate the tasks that are to be performed by the user like
creating an invoice, or creating a journal entry as needed. The generative AI
uses the workers called agents to perform the tasks requested by the user.
These agents either work individually or combine with other agents to
accomplish a certain task. These perform all round activities starting from
planning resource allocation curating compilation and accomplish a task. These
are extremely cool oriented and are designed to perform a specific duty. These
agents are largely autonomous and do not need any human intervention to perform
their task. T can manage the entire operations end to end starting from
interacting with the user in understanding the requirement using prompt
engineering and other techniques to identifying the specific agent or
performing the specialized task planning the resources and eventually
completing the task. Based on the operations performed by these agents they are
broadly classified into four different types –
The first type is the conversational agent. As
the name implies, these agents are used to interact with the end user for
gathering the requirements. Depending upon the business model these end users
can either be a human or another piece of software or agent that would ask a
specific task to be done. These largely use LLMs to understand the requirement
in case they are interacting with other humans to translate them into machine
understandable language.
The next type is the supervisor agent. The
responsibility of this agent is to gather the information received from the
conversational agent and analyze the need and guide the underlying agents to
perform the required task. It is this agent which determines the functional
requirement and the eventual task that is requested. Upon identifying these
needs the right functional and utility agents are identified and the task
assigned to them, respectively. These agents at times take decision on behalf
of humans to determine the right path forward.
The functional agents are the workers who are
associated with one specific domain such as purchasing or payables or general
Ledger etcetera. These have the functionalities recorded in them that defines
how the module is expected to work. They have all the steps, and the rules
recorded in them along with various configurations that will be leveraged by
the underlying agents to perform the requested task. Some of the real-world
examples of such functional agents are customer support agent, which provides interaction
with the customers in answering specific questions or summary agent which
reaches through a larger passage and identifies the gist of it, etc.
The last type is the utility agent. This is the
ground level worker who performs the actual task. These agents are strictly
associated with a narrow set of capabilities and tools depending upon the
requirement each of these agents are invoked to perform a particular task. For
instance, come on if the request is to generate a code for a specific
interface, in PLSQL, there will be a specific utility agent that will be
triggered which was associated with this language and coding methodologies to
generate the code. Similarly, there are many such utilities to actively assist
generative AI in best serving the user.
3. Benefits of Generative AI in Applications
There are several benefits in leveraging these
AI features in the business applications. Since these solutions have dedicated
agents underneath them who have specialized task focused on a particular module
or an activity one can be confident that the solution provided by these agents
are accurate. These generative eggs can be used in various domains and
functional tracks within the organization helping them to navigate easier and
produce data-driven results.
In the General Ledger domain, the generative AI
can be used to track the journal entries and out of balance entries to find the
problems within. These specialized agents can dig through the balance sheets of
any particular. Or the application as a whole to identify anomalies and bring
them to user attention. There are also special utility agents that are designed
to perform the day-to-day tasks that these business users perform such as
creation of journals running reports set up activities etc. The error handling
feature can assist the end users to understand the problems that they might be
facing as part of the D-Day activities. Using a natural language questions, the
users can track and question about a particular transaction to find the status
at various stages and the problematic area that needs to be addressed. This
helps both the business as well as the support team in easily identifying the
errors and taking corrective actions.
The provisions such as predefined chat bots
another interactive AI solution would come handy in automating the customer
service operations. By educating the agents and providing enough data samplers
these chat bots can effectively interact with the end users and the customers
to understand their needs and provide responsible solutions. This can also be
tuned in such a way that they can reroute it to other channels or agents or
even a human customer representative for resolving the customer needs. This is
a great way forward in terms of customer experience and other interactive
services that the organization might need.
The OCI Language feature is yet another
provision provided by Oracle as part of their Gen. AI feature. Using this
feature the system can read any paper document that scanned or images and
identify keywords and patterns and extract them and translate them into the
respective relational objects. For instance, a scanned invoice received from a
supplier can be fit through the generative AI solution to extract the invoice
numbers the vendor information the invoice amount and backing details if any to
identify the payables related information pertaining to this document. This
comes in especially handy in terms of interacting with the windows and other
third parties who might not have a digital interface with the company.
The document understanding feature provided by
Oracle Gen. AI is the ability of these systems to read the document and
understand complex business processes. This leverages the sophisticated utility
agents which can scan through the documents and understand the sequence of
activity status listed in the document and clearly provide a concise and
sequential flow understandable by the end users. This is particularly handy in
terms of understanding any new business process that might be introduced as
part of merger or process automation. Additionally, this feature also helps the
developers and other IT resources in scanning through technical documents and
code in identifying key tables APIs and other technical aspects that might be
used as part of the implementation. The extensibility feature of the Gen. AI
helps the customer to formulate a custom solution that is best fit for their
industry specific needs. This allows the implementers to have a completely
personalized and focused AI solution that caters to every requirement of their
business.
The image recognition feature of the generative
AI enables the system to scan through images and perform various necessary
activities such as image matching search using images, identifying the text
within the images etc. These can also be used in terms of reading some of the
scanned documents where the organization leverages documents attached to mails
or other means for the data entry team or processing team to create
transactions. These activities are usually done manually where there is a human
interaction needed to recognize the patterns and the details as part of the
image and manually enter them into the system with this facility this
eliminates the need for human intervention thereby avoiding any it is
oversights that might happen.
Similar to the image recognition the speech
recognition feature is yet another significant feature that can assist the
business in their day-to-day activities. Using this feature the speech patterns
can be recognized, and notes and transcripts can be created from the meetings
and discussions to provide a concise and clear minute of meeting by capturing
all the key information from the meeting. This eliminates the overhead of the
meeting organizer or other parties to take meticulous notes and share it with the
team which is quite prone two errors and information drops.
4. Conclusion
With Gen AI and Prompt Engineering, the interactions with machines has evolved from monotonic response to more interactive and diverse, where the response generated in more of a process, rather than a simple question and answer. Though it is still experimental, these technologies have shown the capabilities of AI, that was never seen before and is evident that it is more to come. In recent years, every organization has added AI as part of their IT goals and are actively pursuing solutions best suited for them. Understanding the demand and the potential, all IT product and service companies are competing against each other and have invested heavily on improving these technologies to provide more accurate and advanced capabilities. Even in its experimental form, the Gen AI and Prompt Engineering have been implemented by many companies in various capacity, particularly in the areas like customer service, sales, marketing, etc. While have had amazing success in stable environments, the reliability in more dynamic situations is still patchy. With additional sources, new set of details will influence the ability of these Gen AI technologies, which can end up getting better or worse, depending on the user’s perspective and its ability to gauge and provide consistent results in any environment is yet to be judged.
5.References