Conquering generative AI: How to scale large language models

The grand adventure of generative AI (gen AI) and implementing large language models (LLMs) at scale – it's like riding a dragon through the tech realm, filled with highs, lows, and the occasional fire-breathing challenge.

Success in this quest lies in skillful navigation. Here, we share best practices so you can mitigate the risks and overcome challenges to scale your AI operations.

As you embark on the quest to implement gen AI at scale, remember – it's a journey of epic proportions. Organizations must navigate mountains of information before they can understand, embrace, and responsibly use this technological marvel.

That's why Genpact, in partnership with NASSCOM, developed a playbook on everything you need to know about making a winning generative AI solution. Here are some key considerations:

• Fairness: Trained on vast datasets, LLMs may inadvertently create inequities, biases, and discrimination. Overseeing ethical AI requires constant vigilance. Without it, the consequences can be severe
• Intellectual property (IP): Ownership ambiguity when AI generates content can lead to legal complications. Defining and protecting IP rights in this evolving landscape demands adaptive safeguards to navigate the intersection of technology and originality
• Privacy: Implementing stringent safeguards is crucial to prevent inadvertent leaks and protect individuals' privacy, such as avoiding unauthorized access to personally identifiable information
• Security: Safeguarding against potential breaches, protecting sensitive data, and fortifying defenses to counter evolving cyberthreats are essential to deploying gen AI responsibly
• Explainability: Transparency into how AI models make decisions is crucial for user trust and ethical deployment. Developing methods to make gen AI more interpretable is an ongoing pursuit for responsible and accountable AI implementation
• Reliability and steerability: LLMs may encounter hallucinations – meaning they generate inaccurate or fictional information based on training data. Mitigating these hallucinations requires robust mechanisms to discern and filter out erroneous information
• Social impact: Biases in training data may disproportionately impact certain groups of people, amplifying existing inequalities. Ethical considerations are vital to ensure the deployment of gen AI fosters inclusivity and benefits all segments of society

As businesses democratize access to generative AI, the stakes for developing fair, trusted solutions are high. However, LLMs, which power gen AI, are like the powerful wizards of language generation. That's why turning LLMs loose without the proper guardrails can backfire.

To guide enterprise AI initiatives, we have built a responsible generative AI framework. Let's look at some of the elements in more detail:

1. Requirement definition: Before gen AI is let loose, organizations must articulate clear objectives, understand diverse use cases, identify target audiences, and navigate the intricate landscape of data requirements
2. Exploratory data analysis (EDA): Best practices dictate a meticulous EDA process, understanding data intricacies, addressing biases, and ensuring its alignment with model objectives. This quest for insights guides organizations toward a successful model implementation
3. Data preparation and prompt engineering: Using diverse, high-quality datasets and crafting well-defined prompts refines model behavior to deliver ethical and contextually relevant outputs. It's also essential to maintain a record of data changes, safeguard data with encryption, and use prompt templates for effective capture and execution
4. Model fine-tuning: Leading organizations know when to guide the model and when to allow it to unfurl its wings of creativity. The delicate balance between control and autonomy is pivotal as the language models evolve
5. Model review and governance: Diligently overseeing the model's outputs, addressing biases, and prioritizing alignment with objectives is critical. This vigilant approach ensures responsible deployment. For example, data engineers can set thresholds and trigger alerts to report and fix latency in metadata
6. Model inference and serving: Heroes fine-tune the serving infrastructure, optimizing it for efficiency and responsiveness, allowing the model's linguistic prowess to unfold gracefully in its quest to transform digital interactions
7. Model monitoring with human feedback: Continuously observing outputs and inviting human evaluators to provide feedback is also key. This iterative loop refines the model's behavior while adjusting for evolving requirements

In banking, preparing credit packs and annual credit reviews is time-consuming and costly. Bank analysts must compile and analyze massive datasets and adhere to regulatory guidelines.

To tackle this challenge, we developed a generative-AI-enabled loan origination and monitoring tool that aggregates vast volumes of documents. Using large language models, it extracts data and segments text to create analysis and summaries.

With the solution processing so much sensitive data, we needed to pinpoint exactly where the information came from. We implemented a method to attribute information, enhancing accuracy and confidence. We also embedded a responsible AI framework to monitor responses.

Now, bank analysts and other stakeholders can make informed decisions quickly and confidently.

By following this framework, you can navigate the complexities of gen AI and scale the technology to unlock its full potential. Here are just some of the rewards you'll reap:

• Democratization of tech: A successful gen AI strategy begins with the liberation of data and technology – a democratization of ideas that transcends traditional silos. With a responsible framework that enables diverse teams to contribute and access AI tools, organizations foster a culture of collaboration and innovation
• High-quality outputs: Rigorous training data curation, meticulous prompt engineering, and continuous fine-tuning ensure that AI outputs meet the highest standards. Striking the right balance between human oversight and machine autonomy guarantees a blend of creativity and accuracy
• Economic gains in resource-intensive projects: Identifying niche markets, optimizing production pipelines, and using AI-generated content for revenue streams come from a well-executed AI strategy. The key lies in aligning AI capabilities with human feedback to capture emerging opportunities
• Setting realistic expectations: Transparency about AI limitations helps stakeholders understand the boundaries of technology, fostering realistic expectations and preventing disillusionment
• Building trust: Open communication about AI's capabilities fosters trust among users and stakeholders. Prioritizing ethical considerations, fairness, and transparency enhances this trust even further
• Reliable partnerships: When you partner with reputable technology experts – with a track record of ethical practices, robust models, and reliable support – you increase the odds of a smooth AI development and deployment
• Continuous improvement thorough auditing: Regularly auditing AI outputs, evaluating performance against predefined metrics, and conducting ethical assessments contribute to ongoing improvement
• Adaptability and responsiveness: Gen AI's effectiveness depends on the quality and relevance of its training data. A successful strategy includes mechanisms for adapting and responding to evolving business demands

The journey of implementing LLMs at scale is an epic tale – a quest guided by the North Star of best practices. The true heroes of innovation stride boldly into uncharted territories, using technology as an ally to boost creativity and inclusivity while responsibly pioneering across the ever-expanding digital frontier.

This point of view is authored by Sreekanth Menon, global AI/ML leader, and Megha Sinha, AI leader.