Information Technology Articles

Article / Updated 08-08-2024

The landscape of contract lifecycle management (CLM) is rapidly evolving with the advent of advanced technologies like generative AI (Gen AI). Gen AI is a new iteration of AI whose key benefit is the generation of new content based on the patterns and information it’s learned from existing datasets. Gen AI isn’t a trend or a fad. It’s a new technology that represents a seismic shift in many ways. Organizations are no longer asking if they should embrace AI in CLM but rather how swiftly and effectively they can adapt. The golden age of powerful intelligent technology must be embraced, and you must adapt to advance your business. Integrating these technologies into your CLM can make your CLM an even more powerful tool. AI is like giving machines a brain to think and learn, while Gen AI is about giving them creativity to make new things. When you apply Gen AI to CLM and your contracting processes, it truly expedites your third-party paper review, contract redlining, playbook review, negotiation, and more. In this article, you discover how Gen AI’s powerful use cases are wielded in CLM. Tackling Gen AI Use Cases that Impact CLM Gen AI streamlines contract creation, analysis, and risk assessment, revolutionizing how businesses manage contracts. It’s an exciting development that promises efficiency and accuracy in CLM processes. Within CLM, Gen AI’s prominent use cases include the following: Drafting your contracts with ease: Transform how your organization handles your contracts and their processes. Creating contracts through traditional methods is a time-consuming process that requires highly trained experts, but Gen AI can flip that old way of doing things and start automating your contract drafting. Gen AI does this by learning from your existing contracts and then generating new ones based on your specific business needs and specific inputs that you provide to the tool. Improved adoption: Gen AI becomes a critical co-pilot, working with your users without requiring training. By adding this resource capacity, you can increase efficiency through automating repetitive processes, such as expedited contract review and risk analysis. Your business can do more and free up valuable human resources to focus on strategic initiatives. While Gen AI is still new and slowly being adopted, the benefits are compelling for businesses to adopt Gen AI faster. Voice and text-activated operation: You can easily communicate your objectives through voice commands or by typing, and Gen AI provides guided, click-free actions to efficiently achieve your goals. Intelligent search: Gen AI is able to review large amounts of data quicker than before, allowing for less time spent on searches and more time achieving precise results faster. It can identify key provisions and the existence of specific business terms across agreements swiftly, making audits or merger and acquisitions (M&A) transactions much easier. Advanced business intelligence: Gen AI offers more robust contextual insights and actionable recommendations, including summaries of data that it then can use to drive more data-driven decisions. These AI insights can help you negotiate better terms, optimize contract structures, and align legal strategies with broader business objectives. Proactive support and risk management: Gen AI facilitates smooth collaboration during document review, and it can proactively identify legal risks, offering recommendations to ensure compliance and mitigate potential issues. In today’s culture, minimizing risk and ensuring compliance are paramount. Gen AI can leverage advanced algorithms to systematically analyze agreements, flag potential compliance issues, and ensure adherence to legal standards. With Gen AI’s contract analysis and risk assessment, your organization can make better informed decisions about its contracts. Using Gen AI Use Cases to Strengthen Your Teams AI-powered CLM use cases provide value in diverse scenarios. By implementing AI contract software, all your teams benefit: Legal: Legal departments can automate contract analysis, strategy development, and negotiations. AI also ensures that contracts comply with the latest legal standards and regulations. Procurement: Procurement teams can automate the vendor contract lifecycle and third-party paper reviews. AI streamlines the creation, review, and approval of contracts, ensuring that procurement processes are seamless and compliant. Sales: Sales teams leverage AI to accelerate the contract negotiation process. By expediting redlining and ensuring the accuracy of contract terms, sales professionals can close deals more efficiently and with reduced risks. Compliance: AI helps you monitor and ensure adherence to contractual obligations. By providing real-time insights into contract performance, AI-enhanced solutions help identify and mitigate risks associated with non-compliance. Expanding Gen AI in CLM with Malbek You’re ready to elevate your CLM experience and unleash the power of Gen AI. You want to maximize the power of your digital contracts, but you need a solid partner along the way. In this section, you learn more about Malbek and how the company can help you do just that. To learn more about Malbek, you can also visit one of these resources: • www.malbek.io • www.malbek.io/platform www.malbek.io/platform Simplify CLM complexity Malbek empowers its customers with a dynamic, centralized, and fully configurable CLM platform that simplifies your CLM processes. CLM can be complex, but with a trusted partner, you can distill critical insights from contracts for actionable decision-making and peak profitability. Accelerate contracting velocity Build and launch contract and approval processes with ease. From intuitive workflows and seamless approvals to swift contract generation, Malbek’s platform empowers enterprises to navigate contracts with unprecedented speed, ensuring efficiency, compliance, and strategic impact at every turn. Unite global teams and improve collaboration Malbek seamlessly integrates with your favorite business apps, such as Salesforce, Microsoft, SAP, NetSuite, Slack, Coupa, OneTrust, Adobe Sign, DocuSign, and more. By connecting your CLM system with the rest of your business, you can maintain a single source of truth and streamline your operations. Improve decision-making and minimize risk Eliminate time-consuming, manual tasks that take away from high-value objectives. With Malbek AI infused throughout the contracting process, you gain immediate access to timely contextual insights and recommendations to have the greatest impact on your business. AI also streamlines negotiations and shortens review cycles. Download your free copy of Contract Lifecycle (CLM) Management For Dummies, Malbek Special Edition today.

Article / Updated 05-31-2024

At work as well as in your personal life, you’ve almost certainly been bombarded with talk about generative artificial intelligence (AI). It’s all over the mainstream media, in trade journals, in C-suite conversations, and on the front lines of whatever work your organization does. There’s no escaping it. The stories make AI sound so miraculous that, in fact, you could be forgiven for thinking it must be a bunch of hype. But the reality is, generative AI can truly be transformational for businesses. You can leave it for textbooks to fill in the details about what AI is and how it works. But in a nutshell, AI relies on building large language models (LLM) with the help of machine learning (ML). AI trains on vast amounts of data, immerses itself, and learns from the data in ways not unlike how humans learn (but a whole lot faster, and ingesting far, far more data). Notice that the title of this article refers to generative AI. This AI doesn’t just make recommendations — it actually creates new data or content, or generates insights by using the power of natural language processing (NLP) and ML. Tackling many tasks What can generative AI really do for your business? What business problems can it solve? For starters, it’s a fantastic headache remedy. Some of the business headaches generative can cure include Production bottlenecks: Got processes that are stuck and unable to keep up with the demands of customers? Generative AI breaks through bottlenecks by automating processes, improving efficiency, facilitating faster and better human decisions, increasing output, maximizing resources, and speeding up development cycles. Tedious tasks: Generative AI can tackle mundane and tedious tasks, freeing up human brainpower for real value-creating initiatives that your people will find more fulfilling. Inconsistencies and noncompliance: Generative AI creates consistency across your organization’s communications and enforces compliance with internal and external standards. It’s easy for discrepancies and errors to pop up and multiply — generative AI can identify these issues, offer insights and recommendations, and even automatically fix them. Training hurdles: Generative AI helps new hires onboard and get up-to-speed quickly by generating training materials and job simulations. Personalized instruction can fill knowledge gaps. Customer-service struggles: When equipped with information-retrieval solutions, the technology can answer questions quickly and can even handle some customer interactions entirely on its own. It also improves live human interactions by empowering agents and creating instant conversation summaries. Exploring the use cases What generative AI can do for your organization boils down to three primary areas: Creating: This is what it sounds like — using AI to come up with something new. It also may mean editing or revising something that has already been created, by a person or AI, perhaps by turning it into a different format. For your marketing team, a generative AI tool can write the first draft of an ebook about a new product, or create a press release or search engine optimization (SEO)-ready web content. It can come up with a knowledge base article on the latest product feature to help the support team, or a best-practices management article for learning and development. It can help the human resources (HR) team write a job description, making sure it’s doing so in inclusive language. The product development team will love how it ingests and crunches a list of features and bug tickets to come up with release notes. Analyzing: This means taking an in-depth look at content of some kind and generating insights. Generative AI can spot trends or reach conclusions of some sort, perhaps even analyze sentiment amid a batch of customer feedback. Marketing may ask the AI platform to process a webinar recording and summarize the key takeaways. The support team can have it scour customer support survey responses to come up with insights on areas of improvement to consider. Generative AI can help learning and development conjure up some FAQs by analyzing and categorizing what’s in an internal wiki. AI can listen to a recording of a job interview and create a summary for a recruiter. Product developers can have it study customer feedback to find insights for what new features to prioritize. Governing: The govern use case includes a focus on compliance, looking for language that runs afoul of legal and regulatory rules. It finds incorrect terminology and statements and works to prevent data loss and global compliance problems. This type of AI work also means checking for factual accuracy, detecting claims that are wrong and suggesting replacement wording. Marketers can use it to find errors and violations in advertising copy, and for HR, AI can flag non-inclusive language in employee communications, then make suggested revisions. The learning and development team may use it to ensure training materials are compliant with industry certification requirements and other vital standards. Making it happen Many generative AI tools are out there right now, and they’re ready for the masses. Countless people subscribe to platforms such as ChatGPT and Google’s Gemini, and Meta AI is now built right into social media platforms. For the use cases outlined in the preceding section, though, it’s essential to seek an enterprise-grade, full-stack generative AI platform rather than a consumer-targeted AI assistant. Your organization will want a platform that can be truly customized to your needs and integrated with your operations, trained on accurate data that’s relevant to your business and industry, and fully in line with your security and compliance requirements. So, do it yourself? That’s not such a great plan, either. Building your own AI stack can be slow and expensive. Look for a partner that can abstract the complexity so you can benefit from the AI-first workflows, not get bogged down building and maintaining infrastructure. When picking a platform, follow these tips: Keep pace with your organizational needs. Get a tool that can deploy custom AI apps in a snap for any use case, including digital assistants, content generation, summarization, and data analysis. Seek the right model. Palmyra LLMs from Writer, for example, are top-ranked on key benchmarks for model performance set by Stanford’s Holistic Evaluation of Language Models. Connect to your company knowledge. An LLM alone can’t deliver accurate answers about information that’s locked inside your business knowledge bases. For that, you need retrieval-augmented generation (RAG), which is basically a way to feed an LLM-based AI app company-specific information that can’t be found in its training data. Check out writer.com/product/graph-based-rag for more information. Be sure it’s fully customizable. You need consistent, high-quality outputs that meet your organization’s specific requirements, and a general consumer tool can’t do that. You also must have AI guardrails that enforce all your rules and standards. Integrate the tool. To fit into your flow, AI apps need to be in your people’s hands however they’re working. You need an enterprise application programming interface (API) and extensions that’ll build tools right into Microsoft Word and Outlook, Google Docs and Chrome, Figma, Contentful, or whatever else your people love to use. Deploy it your way. Look for options that include single-tenant or multi-tenant deployments. Get things done quickly. Look for a platform that can have you up and running in days, not months. Wouldn’t you rather spend your time adopting than tediously building? Keep it secure. Here’s an incredibly vital area where consumer tools can leave your enterprise at great risk. You need an LLM that’s secure, auditable, and never uses your sensitive data in model training. You’ll lose a lot of sleep if your tool doesn’t comply with the standards your organization must follow, whether that means SOC 2 Type II, HIPAA, PCI, GDPR, or CCPA. Find a tool that manages access with single-sign on (SSO), multifactor authentication, and role-based permissions. Writer is the full-stack generative AI platform for enterprises. It empowers your entire organization to accelerate growth, increase productivity, and ensure compliance. For more information on how to transform work with generative AI, download Generative AI For Dummies, Writer Special Edition.

Cheat Sheet / Updated 04-30-2024

As AI tools grow more complex, effectively communicating with them is becoming a necessary skill for most professions. Learning the art of crafting effective prompts unlocks creativity and enhances decision-making abilities. Whether you’re a developer building the latest AI application, a marketer leveraging chatbots, or a writer automating content creation, the skill of writing AI prompts is indispensable. Poorly worded prompts will never yield the results you’re looking for. The good news is, you can practice and improve your prompting skills and find opportunities to advance in your career.

Cheat Sheet / Updated 04-12-2024

A wide range of tools is available that are designed to help big businesses and small take advantage of the data science revolution. Among the most essential of these tools are Microsoft Power BI, Tableau, SQL, and the R and Python programming languages.

Article / Updated 03-26-2024

Interest in artificial intelligence (AI) is growing, and the power of AI can, and should, be leveraged for use in customer experience (CX) to benefit your external and internal stakeholders: your customers, agents, and supervisors. Delighting your customers Your customers have a stake in your business, and you wouldn’t be where you are without them. Today’s external stakeholders — your customers — crave digital self-service, and it drives their interactions. Companies may often underestimate this desire. Customers want and expect (even demand) options that meet their personalized needs for up-to-date information and assistance, and they want this without having to talk directly with another person. With AI, you can help make that happen. AI promises usefulness across all types of customer interactions, including searching for information, using a chatbot, interacting with people, and more. With AI for CX, you can host a safe, secure environment for CX to occur. And keep in mind this little techie tidbit: Roughly 30 percent of transactions were supported by automation in 2023, and about 70 percent will be in 2025. The incorporation of AI into your CX is vital to your brand. The NICE Enlighten Suite is trusted AI for business and utilizes the latest GenAI technology and the largest labeled dataset of omnichannel CX interactions to create positive customer experiences. Within the suite, Enlighten Autopilot, focusing on customers, delivers personalized, business-aligned conversational AI experiences to thrill your customers in the following ways: • Meeting customers on their preferred channels • Seamless engagement across all touchpoints • Providing a consistent and unified experience • Having data-driven decision-making • Strengthening brand perception Visit www.nice.com/websites/CX.AI.NOW/ to learn more. Supporting your staff The use of AI can also impact your internal stakeholders, and those folks include your agents and supervisors. Most employees will see the positive effects of AI, but when you deploy AI for CX within your organization, seek to reassure everyone that you expect the technology to benefit them. Don’t forget to ask for feedback on their experiences with it, too. The benefits of such technology include Improving employee experience: AI has the potential to positively impact your employees throughout your organization. Agents’ work experiences can be improved and optimized, which helps the organization as a whole to operate more efficiently. Better management information: Make key information more accessible to more people within your business, including your supervisors. With AI, this information can be delivered faster and more conveniently than ever. Generative AI as part of the CX is a powerful positive for your agents, supervisors, and even your brand. Your organization may be able to realize higher sales, greater customer satisfaction, and a better brand image by taking advantage emerging technologies. You need a trusted AI solution for your business. Enter Enlighten Copilot. This solution’s primary function is to empower agents and supervisors with in-the-moment assistance and coaching to deliver premium interactions and to make their jobs easier by offering a variety of versatile features designed to elevate CX and drive success. Copilot also delivers security, privacy, and compliance to help you meet the legal, regulatory, and safety concerns of your company. When companies start to offer AI-powered capabilities on their own, these concerns may be ignored simply out of a lack of knowledge of what AI entails. Enlighten Copilot also seeks to strengthen, not replace, your employee base by targeting AI toward highly repetitive, lower-touch and lower-value interactions. That leaves your agents free for the higher-touch, higher-value interactions. Supervisors also benefit from AI-driven tools to free themselves from repetitive management tasks and to improve decision making. Visit get.nice.com/Not-All-AI-Copilots-Are-Equal.html for more information.

Cheat Sheet / Updated 03-22-2024

Generative AI coding tools can improve your productivity as a coder, remind you about syntax, and even help you with testing, debugging, refactoring, and documentation, but it's up to you to know how to use them correctly. Get ten prompt engineering tips that can make the difference between AI spitting out garbage spaghetti code and crafting elegant code that works. AI coding tools present unique challenges and hazards for software development teams, so check out some simple rules to make sure that generative AI doesn't tank your project. Then see what happened when ChatGPT was asked to list the top things human coders do that AI can never replace.

Article / Updated 01-26-2024

In this article you will learn: What enterprise automation is Why you should implement enterprise automation How enterprise automation will impact businesses in the future What is enterprise automation? Enterprise automation increases efficiency by replacing repetitive, manual, and error-prone business processes with intelligent, automated, and more reliable workflows. Enterprise automation is an ongoing and strategic driver of the digital business landscape that seamlessly connects data, applications, and services with people and their organizations. With enterprise automation, companies remove bottlenecks, improve and accelerate the flow of data, empower IT resources with better productivity, and give non-IT business groups the ability to easily self-serve their data needs – across their enterprise. Why enterprise automation? Think about the IT and organizational challenges you face today. You’re experiencing poor agility in certain areas. Meanwhile, the rapid pace of business requires digitalization, cloud adoption, and quick responsiveness. This situation is exactly what you want to aspire to; however, your business processes are sluggish and suffering where data silos persist. Collaboration between teams is more difficult than it should be. Not only is business moving quickly, but the pace of innovation is increasing as well. Your project leaders, your customers, your internal business partners, and your employee expectations are ever increasing. This forces your organization to be more innovative and try new things as you seek to become faster and more efficient. Competitors in your landscape aren’t sitting still. You need to leverage and exploit all the data you can across your enterprise — operational systems, functional applications, and multiple channels of data. All must be harnessed to survive, thrive, and excel in a competitive landscape and to take corporate intelligence to the next level. You recognize that to be agile and responsive, and to achieve these aspirations, you need solutions to be agile and responsive and capable of connecting your entire enterprise, end-to-end. That is what enterprise automation is all about and the inspiration behind Enterprise Automation For Dummies, sponsored by SnapLogic. The future is enterprise automation Enterprise automation done well uniquely combines data integration, app-to-app integration, and API development and management for API-led integrations and delivery of data services. And, it doesn’t have to be complicated. Easy-to-use, event-driven platforms, augmented with graphical (low-code and no-code) user interfaces and AI-assistance, enables you to get up and running sooner to remove manual burdens and automate business processes. To learn more, download Enterprise Automation For Dummies to gain knowledge of integration principles and pick up insights on how to automate and orchestrate data across your enterprise and empower people. Enterprise Automation For Dummies includes case studies with summaries and achieved success metrics. Download Enterprise Automation For Dummies today and chart your future.

Article / Updated 12-05-2023

We depend on machines to produce everyday essentials — such as power, food, and medicine — and to support nearly every aspect of society. Thus, machine health is vital to overall manufacturing and business health. Machine health transforms reliability, maintenance, operations, and asset performance management by using artificial intelligence (AI) and Internet of Things (IoT) technologies to improve performance, reduce downtime, and help manufacturers reach Industry 4.0 standards. Transform the way you work In an era of labor shortages and technology innovation, manufacturing needs to move faster toward digitization. Using AI in manufacturing, companies can eliminate repetitive tasks, reduce inefficiencies, and strengthen data-driven decision making. With insights into the real-time condition of their machines, workers can break away from traditional maintenance schedules and manual tasks. This means more time for proactive work and collaboration with other departments, which leads to stronger cross-functional teams. It also leads to further innovations, such as process optimization. When employees have insights into the health of their machines, they can predict their workdays and own their schedules, opening up new opportunities for innovation and engagement. Learn more about transforming the way you work with machine health at www.augury.com/use-cases/business-goal/transform-the-way-you-work. Eliminate unnecessary downtime Unplanned downtime can be expensive. There’s the cost of the repairs themselves, lost production and sales, and reputational damage. Maintenance and reliability teams often have to scramble to diagnose and fix the problem as quickly as possible — often resulting in overtime pay and expedited shipping costs for emergency spare parts. Sudden and catastrophic machine failures can also harm worker morale and jeopardize worker safety. Thus, reducing unnecessary downtime has a significant impact on both your top and bottom lines. Learn more about eliminating unnecessary downtime with machine health at www.augury.com/use-cases/business-goal/eliminate-unnecessary-downtime. Reduce loss, waste, and emissions Reducing waste to improve sustainability has become a top priority for manufacturers for cost-cutting/efficiency purposes, as well as to promote a healthier planet. Healthy machines can run at capacity and with less downtime, leading to less waste and more efficient energy use. An industry study by the Electric Power Research Institute (EPRI) found that optimizing the performance of rotating assets — which account for approximately 54 percent of U.S. industrial electricity consumption — can reduce energy consumption by 12 to 15 percent. Learn more about reducing loss, waste, and emissions with machine health at www.augury.com/use-cases/business-goal/reduce-loss-waste-and-emissions. Maximize yield and capacity Real-time machine health insights allow maintenance teams to adjust shutdown schedules based on the current condition of the machine, its history, and the recommendations of experts. In the longer-term, machine health minimizes unplanned downtime by improving the health of your machines, reduces planned downtime by deferring nonessential maintenance activities, and can increase output on production lines with healthier machines running optimally. Learn more about maximizing yield and capacity with machine health at www.augury.com/use-cases/business-goal/maximize-yield-and-capacity. Optimize asset care When it comes to asset care — from acquiring and storing parts to managing maintenance resources — manufacturers have traditionally taken a more preventive than predictive approach: Machines are serviced on fixed schedules, regardless of whether or not they need maintenance. Many manufacturers keep spare parts for critical machine assets in inventory because calendar-based maintenance dictates when parts should be replaced — whether or not replacement is needed. The alternative is overspending to get parts on short notice and expensive downtime while you wait for the parts to arrive. Whether hoarding parts or paying more for last-minute parts, both methods are inefficient and costly. Machine health empowers you to control how you spend time and money based on the real-time condition of your machine assets. Learn more about optimizing asset care with machine health at www.augury.com/use-cases/business-goal/optimize-asset-care. Start building a winning machine health culture Machine health can transform operations for every manufacturer. Get your free copy of Machine Health For Dummies at https://www.augury.com/machinehealthfordummies/. How much will you save with Augury Machine Health? Use the ROI calculator at www.augury.com/value-calculator/ to see how much time and money you can save with Augury’s Machine Health.

Article / Updated 12-01-2023

Getting the most out of your unstructured data is an essential task for any organization these days, especially when considering the disparate storage systems, applications, and user locations. So, it’s not an accident that data orchestration is the term that brings everything together. Bringing all your data together shares similarities with conducting an orchestra. Instead of combining the violin, oboe, and cello, this brand of orchestration combines distributed data types from different places, platforms, and locations working as a cohesive entity presented to applications or users anywhere. That’s because historically, accessing high-performance data outside of your computer network was inefficient. Because the storage infrastructure existed in a silo, systems like HPC Parallel (which lets users store and access shared data across multiple networked storage nodes), Enterprise NAS (which allows large-scale storage and access to other networks), and Global Namespace (virtually simplifies network file systems) were limited when it came to sharing. Because each operated independently, the data within each system was siloed making it a problem collaborating with data sets over multiple locations. Collaboration was possible, but too often you lost the ability to have high performance. This Boolean logic decreased potential because having an IT architecture that supported both high performance and collaboration with data sets from different storage silos typically became an either/or decision: You were forced to choose one but never both. What is data orchestration? Data orchestration is the automated process of taking siloed data from multiple data storage systems and locations, combining and organizing it into a single namespace. Then a high-performance file system can place data in the edge service, data center, or cloud service most optimal for the workload. The recent rise of data analytic applications and artificial intelligence (AI) capabilities has accelerated the use of data across different locations and even different organizations. In the next data cycle, organizations will need both high-performance and agility with their data to compete and thrive in a competitive environment. That means data no longer has a 1:1 relationship with the applications and compute environment that generated it. It needs to be used, analyzed, and repurposed with different AI models and alternate workloads, and across a remote, collaborative environment. Hammerspace’s technology makes data available to different foundational models, remote applications, decentralized compute clusters, and remote workers to automate and streamline data-driven development programs, data insights, and business decision making. This capability enables a unified, fast, and efficient global data environment for the entire workflow — from data creation to processing, collaboration, and archiving across edge devices, data centers, and public and private clouds. Control of enterprise data services for governance, security, data protection, and compliance can now be implemented globally at a file-granular level across all storage types and locations. Applications and AI models can access data stored in remote locations while using automated orchestration tools to provide high-performance local access when needed for processing. Organizations can grow their talent pools with access to team members no matter where they reside. Decentralizing the data center Data collection has become more prominent, and the traditional system of centralized data management has limitations. Issues of centralized data storage can limit the amount of data available to applications. Then, there are the high infrastructure costs when multiple applications are needed to manage and move data, multiple copies of data are retained in different storage systems, and more headcount is needed to manage the complex, disconnected infrastructure environment. Such setbacks suggest that the data center is no longer the center of data and storage system constraints should no longer define data architectures. Hammerspace specializes in decentralized environments, where data may need to span two or more sites and possibly one or more cloud providers and regions, and/or where a remote workforce needs to collaborate in real time. It enables a global data environment by providing a unified, parallel global file system. Enabling a global data environment Hammerspace completely revolutionizes previously held notions of how unstructured data architectures should be designed, delivering the performance needed across distributed environments to Free workloads from data silos. Eliminate copy proliferation. Provide direct data access through local metadata to applications and users, no matter where the data is stored. This technology allows organizations to take full advantage of the performance capabilities of any server, storage system, and network anywhere in the world. This capability enables a unified, fast, and efficient global data environment for the entire workflow, from data creation to processing, collaboration, and archiving across edge devices, data centers, and public and private clouds. The days of enterprises struggling with a siloed, distributed, and inefficient data environment are over. It’s time to start expecting more from data architectures with automated data orchestration. Find out how by downloading Unstructured Data Orchestration For Dummies, Hammerspace Special Edition, here.

Article / Updated 10-31-2023

Indeed, prompting is both the easy part and the most difficult part of using a generative artificial intelligence (AI) model, like ChatGPT. Difficulties in the complexity of cues and nuances in text-based prompts are why some organizations have a prompt engineering job role. What is a ChatGPT prompt? It's a phrase or sentence that you write in ChatGPT to initiate a response from the AI. ChatGPT responds based on its existing knowledge base. Don't have time to read the entire article? Jump to the quick read summary. Prompt engineering is the act of crafting an input, which is a deed borne partly of art and partly of logic. And yes, you can do this! However, you might want to practice and polish your prompting skills before you apply for a job. Considering how to prompt ChatGPT, if you have a good command of the subtleties of language and great critical-thinking and problem-solving skills, seasoned with more than a dash of intuitive intelligence, you’ll be amazed at the responses you can tease out of this technology with a single, well-worded prompt. When you prompt ChatGPT, you are embedding the task description in the input (called the prompt) in a natural-language format, rather than entering explicit instructions via computer code. Prompt engineers can be trained AI professionals or people who possess sufficient intuitive intelligence or skills transferrable to crafting the best prompts for ChatGPT (or other generative AI platforms) that solicit the desired outputs. One example of a transferrable skill is a journalist’s ability to tease out the answers they seek in an interview by using direct or indirect methods. Prompt-based learning is a strategy AI engineers use to train large language models. The engineers make the model multipurpose to avoid retraining it for each new language-based task. Currently, the demand for talented writers who know how to write a prompt, or prompt engineers, is very high. However, there is a strong debate as to whether employers should delineate this unique skill as a dedicated job role, a new profession, or a universal skill to be required of most workers, much like typing skills are today. Meanwhile, people are sharing their prompts with other ChatGPT users in several forums. You can see one example on GitHub. How to write a prompt If you enter a basic prompt, you’ll get a bare-bones, encyclopedic-like answer, as shown in the figure below. Do that enough times and you’ll convince yourself that this is just a toy and you can get better results from an internet search engine. This is a typical novice’s mistake and a primary reason why beginners give up before they fully grasp what ChatGPT is and can do. Understand that your previous experience with keywords and search engines does not apply here. To write awesome ChatGPT prompts, you must think of and use ChatGPT in a different way. Think hard about how you’re going to word your prompt. You have many options to consider. You can assign ChatGPT a role or a persona, or several personas and roles if you decide it should respond as a team, as illustrated in the following figure. You can assign yourself a new role or persona as well. Or tell it to address any type of audience, such as a high school graduating class, a surgical team, or attendees at a concert or a technology conference. You can set the stage or situation in great or minimum detail. You can ask a question, give it a command, or require specific behaviors. A prompt, as you can see now, is much more than a question or a command. Your success with ChatGPT hinges on your ability to master crafting a prompt in such a way as to trigger the precise response you seek. Ask yourself these questions as you are writing or evaluating your prompt: Who do you want ChatGPT to be? Where, when, and what is the situation or circumstances you want ChatGPT’s response framed within? Is the question you're entering in the prompt the real question you want it to answer, or were you trying to ask something else? Is the command you're prompting complete enough for ChatGPT to draw from sufficient context to give you a fuller, more complete, and richly nuanced response? And the ultimate question for you to consider: Is your prompt specific and detailed, or vague and meandering? Whichever is the case, that’s what ChatGPT will mirror in its response. ChatGPT’s responses are only as good as your prompt. That’s because the prompt starts a pattern that ChatGPT must then complete. Be intentional and concise about how you present that pattern starter — the prompt. For more details on using ChatGPT, including how to start a chat, reviewing your chat history, and much more, check out my book ChatGPT For Dummies. Thinking in threads Conversations happen when one entity’s expression initiates and influences another entity’s response. Most conversations do not conclude after a simple one-two exchange like this, but rather continue in a flow of responses cued by the interaction with the other participant. The resulting string of messages in a conversation is called a thread. To increase your success with ChatGPT, write prompts as part of a thread rather than as standalone queries. In this way, you'll craft prompts targeted towards the outputs you seek, building one output on another to reach a predetermined end. In other words, you don’t have to pile everything into one prompt. You can write a series of prompts to more precisely direct ChatGPT’s “thought processes.” Basic prompts result in responses that can be too general or vague. When you think in threads, you’re not aiming to craft a series of basic prompts; you’re looking to break down what you seek into prompt blocks that aim ChatGPT’s responses in the direction you want the conversation to go. In effect, you're using serialized prompts to manipulate the content and direction of ChatGPT's response. Does it work all the time? No, of course not. ChatGPT can opt for an entirely different response than expected, repeat an earlier response, or simply hallucinate one. But serialized prompts do work often enough to enable you to keep the conversation targeted and the responses flowing toward the end you seek. You can use this method to shape a single prompt by imagining someone asking for clarification of your thought or question. Write the prompt so that it includes that information, and the AI model will have more of the context it needs to deliver an intelligent and refined answer. ChatGPT will not ask for clarification of your prompt; it will guess at your meaning instead. You’ll typically get better quality responses by clarifying your meaning in the prompt itself at the outset. Chaining prompts and other tips and strategies Here’s a handy list of other tips and refinements to help get you started on the path to mastering the art of the prompt: Plan to spend more time than expected on crafting a prompt. No matter how many times you write prompts, the next one you write won’t be any easier to do. Don’t rush this part. Start by defining the goal. What exactly do you want ChatGPT to deliver? Craft your prompt to push ChatGPT towards that goal; if you know where you want to end up, you’ll be able to craft a prompt that will get you there. Think like a storyteller, not an inquisitor. Give ChatGPT a character or a knowledge level from which it should shape its answer. For example, tell ChatGPT that it's a chemist, an oncologist, a consultant, or any other job role. You can also instruct it to answer as if it were a famous person, such as Churchill, Shakespeare, or Einstein, or a fictional character such as Rocky. Give it a sample of your own writing and instruct ChatGPT to write its answer to your question, or complete the task in the way you would. Remember that any task or thinking exercise (within reason and the law) is fair game and within ChatGPT’s general scope. For example, instruct ChatGPT to check your homework, your kids’ homework, or its own homework. Enter something such as computer code or a text passage in quotation marks and instruct ChatGPT to find errors in it or in the logic behind it. Or skip the homework checking and ask it to help you think instead. Ask it to finish a thought, an exercise, or a mathematical equation that has you stumped. The only limit to what you can ask is your own imagination and whatever few safety rules the AI trainer installed. Be specific. The more details you include in the prompt, the better. Basic prompts lead to basic responses. More specific and concise prompts lead to more detailed responses, more nuanced responses, and better performance in ChatGPT’s responses — and usually well within token limits. Use prompt chains as a way of strategizing. Prompt chaining is a technique used to build chatbots, but we can reimagine it here as a way to develop a strategic plan using combined or serial prompting in ChatGPT. This technique uses multiple prompts to guide ChatGPT through a more complex thought process. You can use multiple prompts as a single input, such as telling ChatGPT it's a team consisting of several members with different roles, all of whom are to answer the one prompt you entered. Or you can use multiple prompts in a sequence in which the output of one becomes the input of the next. In this case, each response builds on the prompt you just entered and the prompts you entered earlier. This type of a prompt chain forms organically, unless you stop ChatGPT from considering earlier prompts in its responses by starting a new chat. Use prompt libraries and tools to improve your prompting. Some examples follows: Check out the Awesome ChatGPT Prompts repository on GitHub at https://github.com/f/awesome-chatgpt-prompts Use a prompt generator to ask ChatGPT to improve your prompt by visiting PromptGenerator. Visit ChatGPT and Bing AI Prompts on GitHub. Use a tool such as Hugging Face’s ChatGPT Prompt Generator. Try specialized prompt templates, such as the curated list for sales and marketing use cases at Tooltester. On GitHub, you can find tons of curated lists in repositories as well as lots of free prompting tools from a variety of sources. Just make sure that you double-check sources, apps, and browser extensions for malware before using or relying on them. Quick Read Summary Writing effective prompts for ChatGPT is both a craft and a science. A prompt is the phrase or sentence that initiates a response from the AI model. To excel in this skill, consider these essential tips. Crafting an artful prompt: A well-crafted prompt is essential to unlock ChatGPT's potential. Think beyond basic questions and commands. You can assign roles or personas to ChatGPT, set the stage, or address different audiences. Prompt engineering: This skill can be highly valuable. It involves creating prompts that draw out the desired responses from the AI. Prompt engineers often have a background in AI, journalism, or other fields where they've honed their ability to solicit specific information. Thinking in threads: Instead of standalone queries, use prompts as part of a conversation thread. This helps you build on previous outputs and guide the AI's responses toward your desired end. Chaining prompts: Connect prompts sequentially to steer ChatGPT's thought process. This approach can lead to more targeted and refined responses. Be patient and put thought into each prompt. Specificity is key: Detailed prompts lead to more detailed and nuanced responses. Avoid vague or meandering instructions, as ChatGPT mirrors the prompt's clarity. Prompt libraries and tools: Leverage existing resources to improve your prompting skills. There are repositories and tools available, like the Awesome ChatGPT Prompts repository on GitHub and Hugging Face's ChatGPT Prompt Generator. The art of imagination Within reasonable and legal limits, you can instruct ChatGPT for various tasks, from checking homework to creative writing. The only boundary is your imagination. In a world where the demand for skilled prompt writers is increasing, your ability to craft the perfect prompt is a valuable asset. By mastering this art, you can unlock the full potential of ChatGPT and guide its responses to meet your specific needs. Hungry for more? Go back and read the article or check out the book.