As one of the most advanced technologies behind generative AI, large language model operations (LLMOps) is a lengthy and complex process. While the steps and solutions are constantly evolving, let's look closely at LLMOps implementation as it stands now.
1. Select the Foundation Models
The first step in LLMOps implementation is to select the foundation model where the entire solution will be built. Using foundation models is necessary because it is incredibly complex, time-consuming, and expensive to train models from scratch, and only a select few entities have the resources to do this. There are two choices: proprietary or open-source models.
These are closed-source foundation models owned by companies with extensive AI budgets and external expert teams. Examples include OpenAI (GPT-3, GPT-4), AI21 Labs (Jurassic-2), and Anthropic (Claude). Using a proprietary model can be beneficial because they are usually larger and better performing than open-source models. They are also typically ready to use out-of-the-box. The downside to proprietary models is that their APIs can be costly and rigid, largely due to their closed-source nature.
The second type of foundation model is an open-source model that is smaller and more limited in scope than proprietary models. The upside is that they tend to be more cost-effective and flexible. Currently, it is standard practice to use HuggingFace, a community hub for hosting and organizing open-source models, to find a foundation model to custom tailor.
2. Add Data and Context
The next step in LLMOps implementation is to infuse data and context into the foundation model. The tools involved in this step include:
- Document Loaders - As the name implies, these tools load data from a source into the model.
- Knowledge Graphs - Knowledge graphs are semantic networks that organize relationships between entities such as objects, events, and concepts, thus providing a contextual structure.
- Text Splitters - These tools handle the complex task of dividing large blocks of text data into semantically meaningful segments like sentences.
- Vector Databases - Also known as vector stores, vector databases store embeddings, often in the form of domain-specific data, in a format ideal for LLM use.
- Retrievers - Retrievers provide documents in response to unstructured queries without storing the documents themselves.
3. Adapt the LLM to Perform Tasks
The model must be adapted for the LLM to perform downstream tasks successfully. While there are many new developments on the horizon, the current options for LLM adaptation include:
- Prompt Engineering - This is the process of structuring inputs in a way that the model can understand.
- Fine-Tuning - Also known as transfer learning, fine-tuning is the process of retaining an existing model for a different but related task using precise data. This is particularly useful for open-source LLMs that are trained on massive amounts of unstructured data and can be fine-tuned on domain-specific data sets.
- External Data - Models often lack contextual info and can become quickly outdated. To prevent hallucinations, the LLMs must have access to relevant external data through connections with agents and other sources.
- Embedding - Embedding is creating vectors by extracting information from the LLMs. This makes it easier to conduct similar search queries.
The next step is to evaluate the model on several levels, including performance, bias, and user satisfaction. A/B testing, or split testing, is a popular experimentation process in which two or more versions are evaluated. Perplexity testing is another common evaluation method that measures the model's predictive capabilities. BLEU, ROUGE, and human evaluations may be performed as well.
Once the model has passed the evaluation stage, it is time to begin orchestration. Orchestration is the multi-step process of getting results from a foundational model through an API. It requires more than simply inputting prompts is more than just inputting prompts. Foundational Model Orchestration (FOMO) coordinates tasks in a foundational model workflow. FOMO solutions can tie LLMs to data systems, facilitate prompt engineering, connect models, allow users to switch models, and perform A/B testing. FOMO solutions include LangChain, Dust, GPT Index, Fixie.ai, and Cognosis.
For the LLM to reason, create a plan to solve a problem, and execute solutions using the right tools, an LLM needs an agent. An agent can be described as a system with complex reasoning and memory that helps LLMs provide more than just simple responses to simple prompts. In general, agents consist of an agent core, memory model, planning model, and tools. Each LLM can have single or multi-agent environments. There are many ways to create agents, but on its most basic level, it is a concentrated LLMOps process within the overall LLMOps implementation. Users can source and fine-tune existing agent models for their specific use cases.
7. Deploy, Monitor, and Improve
LLMs are typically challenging to deploy due to their size and complexity. It is difficult to fit an entire model into a single GPU, the hardware commonly used for deep learning. Users must have specialized GPUs with vast memory and utilize other advanced deployment techniques to achieve optimal performance. Once deployed, LLM applications must be continuously monitored and improved because any changes to the API can impact the LLM's behavior.
Implementing LLMOps 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 implementing LLMOps, contact Encora today!