One of the most popular uses of large language models (LLMs) is for content generation. Businesses can use LLMs to generate customer-facing and internal text-based content such as product descriptions, emails, presentations, articles, code, and more. Behind all of these valuable assets stands large language model operations (LLMOps), the solution responsible for developing, deploying, managing, and optimizing LLMs. Not all LLMs are alike, and the content achievable through each LLM will largely depend on the LLMOps behind the generative AI model.
This guide explains how LLMOps is used in content generation, dives into the impact of LLMOps, and discusses the future of content generation.
How are LLMOps used in content generation?
LLMOps is a lengthy, complex, and continual process that includes the following key steps:
- Selecting the foundation model.
- Adding data and context.
- Adapting the LLM to perform tasks.
- Evaluating the model on several levels, such as performance, bias, and user satisfaction.
- Employing Foundational Model Orchestration (FOMO) to build a workflow that facilitates prompt engineering, can connect models, performs testing,
connects with data systems, and more. - Creating agents so the LLM can reason, create plans to solve problems, and execute solutions using the right tools.
- Deploying, monitoring, and improving the LLM.
The decisions made at each stage of LLMOps will shape the nature and quality of the content the LLM can generate.
Impact of LLMOps on Content Generation
Let’s discuss a few ways in which LLMOps shape LLM content generation.
- Model Selection: At the beginning of LLMOps, a choice must be made between using a proprietary or open-source LLM model. Both models have distinct benefits and challenges when it comes to content generation. If a proprietary model is chosen, users may be able to use the LLM to generate content right away, as proprietary models are often ready to use out-of-the-box, but the initial outputs will likely be highly generalized, possibly outdated, and more likely to include hallucinations. Work must be done to train a proprietary model to generate reliable and accurate domain-specific outputs. As open-source models are typically smaller and more limited in scope than proprietary models, they are more likely to be chosen for particular applications and will require some degree of customization.
- Adding Data and Context: The next step in LLMOps will significantly impact content generation achieved at a later stage. The data and context infused into the foundation model will show up in the content, whether it is an article, email, website copy, or another type of text-based content.
- Adapting the LLM: Tools such as prompt engineering, fine-tuning, embedding, and more will help the model learn to perform content generation tasks. For instance, linking the LLM to external data will prevent hallucinations and improve the relevancy of the outputs.
- Evaluating the LLM: This is the quality control stage of LLMOps for content generation. The model must be tested to detect and address any biases, performance issues, or user experience issues that may exist within the model that would ultimately detract from the quality of the content generated.
- Orchestration: The orchestration stage enables prompt engineering, a valuable process in content generation. This stage also allows users to leverage data systems and switch between models.
- Creating Agents: LLMs must have single or multiple agents in order to create rich content based on reasoning, problem-solving, and other critical thinking skills not necessarily inherent to the LLM.
- Deploying, Monitoring, and Improving: To support advanced content generation, LLMOps must move smoothly throughout these stages, with a continual focus on the latter two: monitoring and improving.
Future of Content Generation with LLMOps
The future of content generation with LLMOps is limitless. This is truly an area where innovation is happening every day at an exceptionally rapid pace. Here are just a few of the many possibilities on the horizon:
- LLMOps and advanced prompt engineering will likely take personalization to entirely new levels, creating content customized for the reader based on unique preferences and context.
- In the future, LLMOps may enable LLMs to orchestrate interactive storytelling experiences that evolve based on user choices.
- LLMOps has the potential to power LLMs that dissolve language barriers, improve translation capabilities, and increase cultural understanding.
Partner with Encora
Encora has a long history of delivering exceptional software engineering & product engineering services across a range of tech-enabled industries. Encora's team of software engineers is experienced with implementing LLMOps and innovating at scale, which is why fast-growing tech companies partner with Encora to outsource product development and drive growth. We are deeply expert in the various disciplines, tools, and technologies that power the emerging economy, and this is one of the primary reasons that clients choose Encora over the many strategic alternatives that they have.
To get help using LLMOps for content, contact Encora today!
