Batch prompt processing engines are software systems that handle multiple prompts or requests at once, rather than one at a time. These engines are designed to efficiently process large groups of prompts for AI models, reducing waiting times and improving resource use. They are commonly used when many users or tasks need to be handled…
Category: AI Infrastructure
Prompt Usage Footprint Metrics
Prompt usage footprint metrics are measurements that track how prompts are used in AI systems, such as how often they are run, how much computing power they consume, and the associated costs or environmental impact. These metrics help organisations monitor and manage the efficiency and sustainability of their AI-driven processes. By analysing this data, teams…
Internal LLM Service Meshes
Internal LLM service meshes are systems designed to manage and coordinate how large language models (LLMs) communicate within an organisation’s infrastructure. They help handle traffic between different AI models and applications, ensuring requests are routed efficiently, securely, and reliably. By providing features like load balancing, monitoring, and access control, these meshes make it easier to…
AI Toolchain Integration Maps
AI Toolchain Integration Maps are visual or structured representations that show how different artificial intelligence tools and systems connect and work together within a workflow. These maps help teams understand the flow of data, the roles of each tool, and the points where tools interact or exchange information. By using such maps, organisations can plan,…
Model Isolation Boundaries
Model isolation boundaries refer to the clear separation between different machine learning models or components within a system. These boundaries ensure that each model operates independently, reducing the risk of unintended interactions or data leaks. They help maintain security, simplify debugging, and make it easier to update or replace models without affecting others.
Self-Describing API Layers
Self-describing API layers are parts of an application programming interface that provide information about themselves, including their structure, available endpoints, data types, and usage instructions. This means a developer or system can inspect the API and understand how to interact with it without needing external documentation. Self-describing APIs make integration and maintenance easier, as changes…
Agent Scaling Strategies
Agent scaling strategies refer to methods used to increase the number or capability of software agents, such as chatbots or automated assistants, so they can handle more tasks or users at once. These strategies might involve distributing agents across multiple servers, optimising their performance, or coordinating many agents to work together efficiently. The goal is…
Prompt Caching at Edge
Prompt caching at edge refers to storing the results of frequently used AI prompts on servers located close to users, known as edge servers. This approach reduces the need to send identical requests to central servers, saving time and network resources. By keeping commonly requested data nearby, users experience faster response times and less delay…
Containerised LLM Workflows
Containerised LLM workflows refer to running large language models (LLMs) inside isolated software environments called containers. Containers package up all the code, libraries, and dependencies needed to run the model, making deployment and scaling easier. This approach helps ensure consistency across different computers or cloud services, reducing compatibility issues and simplifying updates.
Multi-Tenant Model Isolation
Multi-tenant model isolation is a way of designing software systems so that data and resources belonging to different customers, or tenants, are kept separate and secure. This approach ensures that each tenant can only access their own information, even though they are all using the same underlying system. It is especially important in cloud applications,…